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Kim H, Won K, Ahn M, Jun SC. Comparison of recognition methods for an asynchronous (un-cued) BCI system: an investigation with 40-class SSVEP dataset. Biomed Eng Lett 2024; 14:617-630. [PMID: 38645586 PMCID: PMC11026332 DOI: 10.1007/s13534-024-00357-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Revised: 01/16/2024] [Accepted: 01/24/2024] [Indexed: 04/23/2024] Open
Abstract
Steady-state visual evoked potential (SSVEP)-based brain-computer Interface (BCI) has demonstrated the potential to manage multi-command targets to achieve high-speed communication. Recent studies on multi-class SSVEP-based BCI have focused on synchronous systems, which rely on predefined time and task indicators; thus, these systems that use passive approaches may be less suitable for practical applications. Asynchronous systems recognize the user's intention (whether or not the user is willing to use systems) from brain activity; then, after recognizing the user's willingness, they begin to operate by switching swiftly for real-time control. Consequently, various methodologies have been proposed to capture the user's intention. However, in-depth investigation of recognition methods in asynchronous BCI system is lacking. Thus, in this work, three recognition methods (power spectral density analysis, canonical correlation analysis (CCA), and support vector machine (SVM)) used widely in asynchronous SSVEP BCI systems were explored to compare their performance. Further, we categorized asynchronous systems into two approaches (1-stage and 2-stage) based upon the recognition process's design, and compared their performance. To do so, a 40-class SSVEP dataset collected from 40 subjects was introduced. Finally, we found that the CCA-based method in the 2-stage approach demonstrated statistically significantly higher performance with a sensitivity of 97.62 ± 02.06%, specificity of 76.50 ± 23.50%, and accuracy of 75.59 ± 10.09%. Thus, it is expected that the 2-stage approach together with CCA-based recognition and FB-CCA classification have good potential to be implemented in practical asynchronous SSVEP BCI systems.
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Affiliation(s)
- Heegyu Kim
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Bukgu, Gwangju, 61005 Korea
| | - Kyungho Won
- Hybrid Team, Inria, Univ Rennes, IRISA, CNRS, F35000 Rennes, France
| | - Minkyu Ahn
- School of Computer Science and Electrical Engineering, Handong Global University, Bukgu, Pohang, 37554 Korea
| | - Sung Chan Jun
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Bukgu, Gwangju, 61005 Korea
- School of Artificial Intelligence, Gwangju Institute of Science and Technology, Bukgu, Gwangju, 61005 Korea
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Diez P, Orosco L, Garcés Correa A, Carmona L. Assessment of visual fatigue in SSVEP-based brain-computer interface: a comprehensive study. Med Biol Eng Comput 2024; 62:1475-1490. [PMID: 38267740 DOI: 10.1007/s11517-023-03000-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Accepted: 12/13/2023] [Indexed: 01/26/2024]
Abstract
Fatigue deteriorates the performance of a brain-computer interface (BCI) system; thus, reliable detection of fatigue is the first step to counter this problem. The fatigue evaluated by means of electroencephalographic (EEG) signals has been studied in many research projects, but widely different results have been reported. Moreover, there is scant research when considering the fatigue on steady-state visually evoked potential (SSVEP)-based BCI. Therefore, nowadays, fatigue detection is not a completely solved topic. In the current work, the issues found in the literature that led to the differences in the results are identified and saved by performing a new experiment on an SSVEP-based BCI system. The experiment was long enough to produce fatigue in the users, and different SSVEP stimulation ranges were used. Additionally, the EEG features commonly reported in the literature (EEG rhythms powers, SNR, etc.) were calculated as well as newly proposed features (spectral features and Lempel-Ziv complexity). The analysis was carried out on O1, Oz and O2 channels. This work found a tendency of displacement from high-frequency rhythms to low-frequency ones, and thus, better EEG features should present a similar behaviour. Then, the 'relative power' of EEG rhythms, the rates (θ + α)/β, α/β and θ/β, some spectral features (central and mean frequencies, asymmetry and kurtosis coefficients, etc.) and Lempel-Ziv complexity are proposed as reliable EEG features for fatigue detection. Hence, this set of features may be used to construct a more trustworthy fatigue index.
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Affiliation(s)
- Pablo Diez
- Instituto de Bioingeniería (INBIO), Facultad de Ingeniería, Universidad Nacional de San Juan (UNSJ), San Juan, Argentina.
- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina.
| | - Lorena Orosco
- Instituto de Bioingeniería (INBIO), Facultad de Ingeniería, Universidad Nacional de San Juan (UNSJ), San Juan, Argentina
- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina
| | - Agustina Garcés Correa
- Instituto de Bioingeniería (INBIO), Facultad de Ingeniería, Universidad Nacional de San Juan (UNSJ), San Juan, Argentina
- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina
| | - Luciano Carmona
- Instituto de Bioingeniería (INBIO), Facultad de Ingeniería, Universidad Nacional de San Juan (UNSJ), San Juan, Argentina
- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina
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Guerreiro Fernandes FD, Raemaekers MAH, Freudenburg ZV, Ramsey NF. Considerations for implanting speech brain computer interfaces based on functional magnetic resonance imaging. J Neural Eng 2024. [PMID: 38648782 DOI: 10.1088/1741-2552/ad4178] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/25/2024]
Abstract
PURPOSE
Brain-Computer Interfaces (BCIs) have the potential to reinstate lost communication faculties. Results from speech decoding studies indicate that a usable speech BCI based on activity in the sensorimotor cortex (SMC) can be achieved using subdurally implanted electrodes. However, the optimal characteristics for a successful speech implant are largely unknown. We address this topic in a high field blood oxygenation level dependent (BOLD) functional Magnetic Resonance Imaging (fMRI) study, by assessing the decodability of spoken words as a function of hemisphere, gyrus, sulcal depth, and position along the ventral/dorsal-axis.
Methods:
Twelve subjects conducted a 7T fMRI experiment in which they pronounced 6 different pseudo-words over 6 runs. We divided the SMC by hemisphere, gyrus, sulcal depth, and position along the ventral/dorsal axis. Classification was performed on in these SMC areas using multiclass Support Vector Machine (SVM).
Results:
Significant classification was possible from the SMC, but no preference for the left or right hemisphere, nor for the precentral or postcentral gyrus for optimal word classification was detected. Classification while using information from the cortical surface was slightly better than when using information from deep in the central sulcus, and was highest within the ventral 50% of SMC. Confusion matrices where highly similar across the entire SMC. An SVM-searchlight analysis revealed significant classification in the superior temporal gyrus in addition to the SMC.
Conclusion:
The current results support a unilateral implant using surface electrodes, covering the ventral 50% of the SMC. The added value of depth electrodes is unclear. We did not observe evidence for variations in the qualitative nature of information across SMC. The current results need to be confirmed in paralyzed patients performing attempted speech.
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Affiliation(s)
| | - M A H Raemaekers
- Department of Neurology and Neurosurgery, University Medical Centre Utrecht Brain Centre, Heidelberglaan 100, Utrecht, 3584 CX, NETHERLANDS
| | - Zachary V Freudenburg
- Department of Neurology and Neurosurgery, University Medical Centre Utrecht Brain Centre, Heidelberglaan 100, Utrecht, 3584 CX, NETHERLANDS
| | - N F Ramsey
- Department of Neurology and Neurosurgery, University Medical Centre Utrecht Brain Centre, Heidelberglaan 100, Utrecht, 3584 CX, NETHERLANDS
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Della Vedova G, Proverbio AM. Neural signatures of imaginary motivational states: desire for music, movement and social play. Brain Topogr 2024:10.1007/s10548-024-01047-1. [PMID: 38625520 DOI: 10.1007/s10548-024-01047-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Accepted: 03/12/2024] [Indexed: 04/17/2024]
Abstract
The literature has demonstrated the potential for detecting accurate electrical signals that correspond to the will or intention to move, as well as decoding the thoughts of individuals who imagine houses, faces or objects. This investigation examines the presence of precise neural markers of imagined motivational states through the combining of electrophysiological and neuroimaging methods. 20 participants were instructed to vividly imagine the desire to move, listen to music or engage in social activities. Their EEG was recorded from 128 scalp sites and analysed using individual standardized Low-Resolution Brain Electromagnetic Tomographies (LORETAs) in the N400 time window (400-600 ms). The activation of 1056 voxels was examined in relation to the 3 motivational states. The most active dipoles were grouped in eight regions of interest (ROI), including Occipital, Temporal, Fusiform, Premotor, Frontal, OBF/IF, Parietal, and Limbic areas. The statistical analysis revealed that all motivational imaginary states engaged the right hemisphere more than the left hemisphere. Distinct markers were identified for the three motivational states. Specifically, the right temporal area was more relevant for "Social Play", the orbitofrontal/inferior frontal cortex for listening to music, and the left premotor cortex for the "Movement" desire. This outcome is encouraging in terms of the potential use of neural indicators in the realm of brain-computer interface, for interpreting the thoughts and desires of individuals with locked-in syndrome.
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Affiliation(s)
- Giada Della Vedova
- Cognitive Electrophysiology lab, Dept. of Psychology, University of Milano, Bicocca, Italy
| | - Alice Mado Proverbio
- Cognitive Electrophysiology lab, Dept. of Psychology, University of Milano, Bicocca, Italy.
- NeuroMI, Milan Center for Neuroscience, Milan, Italy.
- Department of Psychology of University of Milano-Bicocca, Piazza dell'Ateneo nuovo 1, Milan, 20162, Italy.
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Akuthota S, K R, Ravichander J. Artifact removal and motor imagery classification in EEG using advanced algorithms and modified DNN. Heliyon 2024; 10:e27198. [PMID: 38560190 PMCID: PMC10980936 DOI: 10.1016/j.heliyon.2024.e27198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Revised: 02/21/2024] [Accepted: 02/26/2024] [Indexed: 04/04/2024] Open
Abstract
This paper presents an advanced approach for EEG artifact removal and motor imagery classification using a combination of Four Class Iterative Filtering and Filter Bank Common Spatial Pattern Algorithm with a Modified Deep Neural Network (DNN) classifier. The research aims to enhance the accuracy and reliability of BCI systems by addressing the challenges posed by EEG artifacts and complex motor imagery tasks. The methodology begins by introducing FCIF, a novel technique for ocular artifact removal, utilizing iterative filtering and filter banks. FCIF's mathematical formulation allows for effective artifact mitigation, thereby improving the quality of EEG data. In tandem, the FC-FBCSP algorithm is introduced, extending the Filter Bank Common Spatial Pattern approach to handle four-class motor imagery classification. The Modified DNN classifier enhances the discriminatory power of the FC-FBCSP features, optimizing the classification process. The paper showcases a comprehensive experimental setup, featuring the utilization of BCI Competition IV Dataset 2a & 2b. Detailed preprocessing steps, including filtering and feature extraction, are presented with mathematical rigor. Results demonstrate the remarkable artifact removal capabilities of FCIF and the classification prowess of FC-FBCSP combined with the Modified DNN classifier. Comparative analysis highlights the superiority of the proposed approach over baseline methods and the method achieves the mean accuracy of 98.575%.
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Affiliation(s)
- Srinath Akuthota
- Department of Electronics & Communication Engineering, SR University, Warangal-506371, Telangana, India
| | - RajKumar K
- Department of Electronics & Communication Engineering, SR University, Warangal-506371, Telangana, India
| | - Janapati Ravichander
- Department of Electronics & Communication Engineering, SR University, Warangal-506371, Telangana, India
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Clemente L, La Rocca M, Paparella G, Delussi M, Tancredi G, Ricci K, Procida G, Introna A, Brunetti A, Taurisano P, Bevilacqua V, de Tommaso M. Exploring Aesthetic Perception in Impaired Aging: A Multimodal Brain-Computer Interface Study. Sensors (Basel) 2024; 24:2329. [PMID: 38610540 PMCID: PMC11014209 DOI: 10.3390/s24072329] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Revised: 04/03/2024] [Accepted: 04/03/2024] [Indexed: 04/14/2024]
Abstract
In the field of neuroscience, brain-computer interfaces (BCIs) are used to connect the human brain with external devices, providing insights into the neural mechanisms underlying cognitive processes, including aesthetic perception. Non-invasive BCIs, such as EEG and fNIRS, are critical for studying central nervous system activity and understanding how individuals with cognitive deficits process and respond to aesthetic stimuli. This study assessed twenty participants who were divided into control and impaired aging (AI) groups based on MMSE scores. EEG and fNIRS were used to measure their neurophysiological responses to aesthetic stimuli that varied in pleasantness and dynamism. Significant differences were identified between the groups in P300 amplitude and late positive potential (LPP), with controls showing greater reactivity. AI subjects showed an increase in oxyhemoglobin in response to pleasurable stimuli, suggesting hemodynamic compensation. This study highlights the effectiveness of multimodal BCIs in identifying the neural basis of aesthetic appreciation and impaired aging. Despite its limitations, such as sample size and the subjective nature of aesthetic appreciation, this research lays the groundwork for cognitive rehabilitation tailored to aesthetic perception, improving the comprehension of cognitive disorders through integrated BCI methodologies.
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Affiliation(s)
- Livio Clemente
- Translational Biomedicine and Neuroscience (DiBraiN) Department, University of Bari, 70124 Bari, Italy; (L.C.); (G.P.); (M.D.); (G.T.); (K.R.); (G.P.); (A.I.); (P.T.)
| | - Marianna La Rocca
- Interateneo Department of Fisica ‘M. Merlin’, University of Bari, 70125 Bari, Italy;
- Laboratory of Neuroimaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA 90033, USA
| | - Giulia Paparella
- Translational Biomedicine and Neuroscience (DiBraiN) Department, University of Bari, 70124 Bari, Italy; (L.C.); (G.P.); (M.D.); (G.T.); (K.R.); (G.P.); (A.I.); (P.T.)
| | - Marianna Delussi
- Translational Biomedicine and Neuroscience (DiBraiN) Department, University of Bari, 70124 Bari, Italy; (L.C.); (G.P.); (M.D.); (G.T.); (K.R.); (G.P.); (A.I.); (P.T.)
| | - Giusy Tancredi
- Translational Biomedicine and Neuroscience (DiBraiN) Department, University of Bari, 70124 Bari, Italy; (L.C.); (G.P.); (M.D.); (G.T.); (K.R.); (G.P.); (A.I.); (P.T.)
| | - Katia Ricci
- Translational Biomedicine and Neuroscience (DiBraiN) Department, University of Bari, 70124 Bari, Italy; (L.C.); (G.P.); (M.D.); (G.T.); (K.R.); (G.P.); (A.I.); (P.T.)
| | - Giuseppe Procida
- Translational Biomedicine and Neuroscience (DiBraiN) Department, University of Bari, 70124 Bari, Italy; (L.C.); (G.P.); (M.D.); (G.T.); (K.R.); (G.P.); (A.I.); (P.T.)
| | - Alessandro Introna
- Translational Biomedicine and Neuroscience (DiBraiN) Department, University of Bari, 70124 Bari, Italy; (L.C.); (G.P.); (M.D.); (G.T.); (K.R.); (G.P.); (A.I.); (P.T.)
| | - Antonio Brunetti
- Electrical and Information Engineering Department, Polytechnic of Bari, 70125 Bari, Italy; (A.B.); (V.B.)
| | - Paolo Taurisano
- Translational Biomedicine and Neuroscience (DiBraiN) Department, University of Bari, 70124 Bari, Italy; (L.C.); (G.P.); (M.D.); (G.T.); (K.R.); (G.P.); (A.I.); (P.T.)
| | - Vitoantonio Bevilacqua
- Electrical and Information Engineering Department, Polytechnic of Bari, 70125 Bari, Italy; (A.B.); (V.B.)
| | - Marina de Tommaso
- Translational Biomedicine and Neuroscience (DiBraiN) Department, University of Bari, 70124 Bari, Italy; (L.C.); (G.P.); (M.D.); (G.T.); (K.R.); (G.P.); (A.I.); (P.T.)
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Soldado-Magraner J, Antonietti A, French J, Higgins N, Young MJ, Larrivee D, Monteleone R. Applying the IEEE BRAIN neuroethics framework to intra-cortical brain-computer interfaces. J Neural Eng 2024; 21:022001. [PMID: 38537269 DOI: 10.1088/1741-2552/ad3852] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Accepted: 03/27/2024] [Indexed: 04/05/2024]
Abstract
Objective. Brain-computer interfaces (BCIs) are neuroprosthetic devices that allow for direct interaction between brains and machines. These types of neurotechnologies have recently experienced a strong drive in research and development, given, in part, that they promise to restore motor and communication abilities in individuals experiencing severe paralysis. While a rich literature analyzes the ethical, legal, and sociocultural implications (ELSCI) of these novel neurotechnologies, engineers, clinicians and BCI practitioners often do not have enough exposure to these topics.Approach. Here, we present the IEEE Neuroethics Framework, an international, multiyear, iterative initiative aimed at developing a robust, accessible set of considerations for diverse stakeholders.Main results. Using the framework, we provide practical examples of ELSCI considerations for BCI neurotechnologies. We focus on invasive technologies, and in particular, devices that are implanted intra-cortically for medical research applications.Significance. We demonstrate the utility of our framework in exposing a wide range of implications across different intra-cortical BCI technology modalities and conclude with recommendations on how to utilize this knowledge in the development and application of ethical guidelines for BCI neurotechnologies.
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Affiliation(s)
- Joana Soldado-Magraner
- Department of Electrical and Computer Engineering and the Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, United States of America
| | - Alberto Antonietti
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milano 20131, Italy
| | - Jennifer French
- Neurotech Network, St. Petersburg, FL 33733, United States of America
| | - Nathan Higgins
- School of Psychological Sciences, Monash University, Clayton, VIC 3800, Australia
| | - Michael J Young
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, United States of America
| | - Denis Larrivee
- Mind and Brain Institute, University of Navarra Medical School, Pamplona, Navarra 31008, Spain
- Loyola University, Chicago, IL 60611, United States of America
| | - Rebecca Monteleone
- Disability Studies Program, University of Toledo, Toledo, OH 43606, United States of America
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Lo YT, Lim MJR, Kok CY, Wang S, Blok SZ, Ang TY, Ng VYP, Rao JP, Chua KSG. Neural interface-based motor neuroprosthesis in post-stroke upper limb neurorehabilitation: An individual patient data meta-analysis. Arch Phys Med Rehabil 2024:S0003-9993(24)00910-9. [PMID: 38579958 DOI: 10.1016/j.apmr.2024.04.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 03/28/2024] [Accepted: 04/01/2024] [Indexed: 04/07/2024]
Abstract
OBJECTIVE To determine the efficacy of neural interface-, including brain-computer interface (BCI), based neurorehabilitation through conventional and individual patient data (IPD) meta-analysis, and to assess clinical parameters associated with positive response to neural interface-based neurorehabilitation. DATA SOURCES PubMed, EMBASE, and Cochrane Library databases up to February 2022 were reviewed. STUDY SELECTION Studies using neural interface-controlled physical effectors (FES and/or powered exoskeletons) and reported Fugl-Meyer Assessment-upper extremity (FMA-UE) scores were identified. This meta-analysis was prospectively registered on PROSPERO (#CRD42022312428). PRISMA guidelines were followed. DATA EXTRACTION Change in FMA-UE scores were pooled to estimate the mean effect size. Subgroup analyses were performed on clinical parameters and neural interface parameters with both study-level variables and IPD. DATA SYNTHESIS Forty-six studies containing 617 patients were included. Twenty-nine studies involving 214 patients reported IPD. FMA-UE score increased by a mean of 5.23 (95% CI: 3.85 to 6.61). Systems that used motor attempt resulted in greater FMA-UE gain than motor imagery, as did training lasting >4 versus ≤4 weeks. On IPD analysis, the mean time-to-improvement above MCID was 12 weeks (95% CI: 7 to not reached). At 6 months, 58% improved above MCID (95% CI: 41 to 70%). Patients with severe impairment (p=0.042) and age >50 years (p=0.0022) correlated with the failure to improve above the MCID on univariate log-rank tests. However, these factors were only borderline significant on multivariate Cox analysis (HR 0.15, p = 0.08 and HR 0.47, p = 0.06, respectively). CONCLUSION Neural interface-based motor rehabilitation resulted in significant though modest reductions in post-stroke impairment and should be considered for wider applications in stroke neurorehabilitation.
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Affiliation(s)
- Yu Tung Lo
- Department of Neurosurgery, National Neuroscience Institute, Singapore; Duke-NUS Medical School, Singapore.
| | | | - Chun Yen Kok
- Department of Neurosurgery, National Neuroscience Institute, Singapore
| | - Shilin Wang
- Department of Neurosurgery, National Neuroscience Institute, Singapore
| | | | - Ting Yao Ang
- Department of Neurosurgery, National Neuroscience Institute, Singapore
| | | | - Jai Prashanth Rao
- Department of Neurosurgery, National Neuroscience Institute, Singapore; Duke-NUS Medical School, Singapore
| | - Karen Sui Geok Chua
- National University of Singapore, Yong Loo Lin School of Medicine, Singapore; Institute of Rehabilitation Excellence, Tan Tock Seng Hospital Rehabilitation Centre, Singapore; Lee Kong Chian School of Medicine, Nanyang Technological University
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Chunduri V, Aoudni Y, Khan S, Aziz A, Rizwan A, Deb N, Keshta I, Soni M. Multi-scale spatiotemporal attention network for neuron based motor imagery EEG classification. J Neurosci Methods 2024; 406:110128. [PMID: 38554787 DOI: 10.1016/j.jneumeth.2024.110128] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Revised: 03/11/2024] [Accepted: 03/25/2024] [Indexed: 04/02/2024]
Abstract
BACKGROUND In recent times, the expeditious expansion of Brain-Computer Interface (BCI) technology in neuroscience, which relies on electroencephalogram (EEG) signals associated with motor imagery, has yielded outcomes that rival conventional approaches, notably due to the triumph of deep learning. Nevertheless, the task of developing and training a comprehensive network to extract the underlying characteristics of motor imagining EEG data continues to pose challenges. NEW METHOD This paper presents a multi-scale spatiotemporal self-attention (SA) network model that relies on an attention mechanism. This model aims to classify motor imagination EEG signals into four classes (left hand, right hand, foot, tongue/rest) by considering the temporal and spatial properties of EEG. It is employed to autonomously allocate greater weights to channels linked to motor activity and lesser weights to channels not related to movement, thus choosing the most suitable channels. Neuron utilises parallel multi-scale Temporal Convolutional Network (TCN) layers to extract feature information in the temporal domain at various scales, effectively eliminating temporal domain noise. RESULTS The suggested model achieves accuracies of 79.26%, 85.90%, and 96.96% on the BCI competition datasets IV-2a, IV-2b, and HGD, respectively. COMPARISON WITH EXISTING METHODS In terms of single-subject classification accuracy, this strategy demonstrates superior performance compared to existing methods. CONCLUSION The results indicate that the proposed strategy exhibits favourable performance, resilience, and transfer learning capabilities.
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Affiliation(s)
- Venkata Chunduri
- Senior Software Developer, Department of Mathematics & Computer Science, Indiana State University, USA
| | - Yassine Aoudni
- Department of Computers and Information Technology, Faculty of sciences and arts, Turaif, Northern Border University, Arar 91431, Kingdom of Saudi Arabia
| | - Samiullah Khan
- Department of Maths, Stats & Computer Science, The University of Agriculture, Peshawar, KP, Pakistan
| | - Abdul Aziz
- Department of Software Engineering, National University of Computer & Emerging Sciences, Islamabad, Pakistan
| | - Ali Rizwan
- Department of Industrial Engineering, Faculty of Engineering, King Abdulaziz University Jeddah 21589, Saudi Arabia
| | - Nabamita Deb
- Department of Information Technology, Gauhati University, India
| | - Ismail Keshta
- Computer Science and Information Systems Department, College of Applied Sciences, AlMaarefa University, Riyadh, Saudi Arabia
| | - Mukesh Soni
- Dr. D. Y. Patil Vidyapeeth, Pune, Dr. D. Y. Patil School of Science & Technology, Tathawade, Pune, India.
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Alsuradi H, Khattak A, Fakhry A, Eid M. Individual-finger motor imagery classification: a data-driven approach with Shapley-informed augmentation. J Neural Eng 2024; 21:026013. [PMID: 38479013 DOI: 10.1088/1741-2552/ad33b3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Accepted: 03/13/2024] [Indexed: 03/23/2024]
Abstract
Objective. Classifying motor imagery (MI) tasks that involve fine motor control of the individual five fingers presents unique challenges when utilizing electroencephalography (EEG) data. In this paper, we systematically assess the classification of MI functions for the individual five fingers using single-trial time-domain EEG signals. This assessment encompasses both within-subject and cross-subject scenarios, supported by data-driven analysis that provides statistical validation of the neural correlate that could potentially discriminate between the five fingers.Approach. We present Shapley-informed augmentation, an informed approach to enhance within-subject classification accuracy. This method is rooted in insights gained from our data-driven analysis, which revealed inconsistent temporal features encoding the five fingers MI across sessions of the same subject. To evaluate its impact, we compare within-subject classification performance both before and after implementing this augmentation technique.Main results. Both the data-driven approach and the model explainability analysis revealed that the parietal cortex contains neural information that helps discriminate the individual five fingers' MI apart. Shapley-informed augmentation successfully improved classification accuracy in sessions severely affected by inconsistent temporal features. The accuracy for sessions impacted by inconsistency in their temporal features increased by an average of26.3%±6.70, thereby enabling a broader range of subjects to benefit from brain-computer interaction (BCI) applications involving five-fingers MI classification. Conversely, non-impacted sessions experienced only a negligible average accuracy decrease of2.01±5.44%. The average classification accuracy achieved is around 60.0% (within-session), 50.0% (within-subject) and 40.0% (leave-one-subject-out).Significance. This research offers data-driven evidence of neural correlates that could discriminate between the individual five fingers MI and introduces a novel Shapley-informed augmentation method to address temporal variability of features, ultimately contributing to the development of personalized systems.
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Affiliation(s)
- Haneen Alsuradi
- Engineering Division, New York University Abu Dhabi, Saadiyat Island, Abu Dhabi 129188, United Arab Emirates
| | - Arshiya Khattak
- Engineering Division, New York University Abu Dhabi, Saadiyat Island, Abu Dhabi 129188, United Arab Emirates
| | - Ali Fakhry
- Engineering Division, New York University Abu Dhabi, Saadiyat Island, Abu Dhabi 129188, United Arab Emirates
| | - Mohamad Eid
- Engineering Division, New York University Abu Dhabi, Saadiyat Island, Abu Dhabi 129188, United Arab Emirates
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Ning M, Duwadi S, Yücel MA, von Lühmann A, Boas DA, Sen K. fNIRS dataset during complex scene analysis. Front Hum Neurosci 2024; 18:1329086. [PMID: 38576451 PMCID: PMC10991699 DOI: 10.3389/fnhum.2024.1329086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Accepted: 03/06/2024] [Indexed: 04/06/2024] Open
Affiliation(s)
- Matthew Ning
- Department of Biomedical Engineering, Neurophotonics Center, Boston University, Boston, MA, United States
| | - Sudan Duwadi
- Department of Biomedical Engineering, Neurophotonics Center, Boston University, Boston, MA, United States
| | - Meryem A. Yücel
- Department of Biomedical Engineering, Neurophotonics Center, Boston University, Boston, MA, United States
| | - Alexander von Lühmann
- Department of Biomedical Engineering, Neurophotonics Center, Boston University, Boston, MA, United States
- BIFOLD – Berlin Institute for the Foundations of Learning and Data, Berlin, Germany
- Intelligent Biomedical Sensing (IBS) Lab, Technical University Berlin, Berlin, Germany
| | - David A. Boas
- Department of Biomedical Engineering, Neurophotonics Center, Boston University, Boston, MA, United States
| | - Kamal Sen
- Department of Biomedical Engineering, Neurophotonics Center, Boston University, Boston, MA, United States
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12
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Kothe C, Hanada G, Mullen S, Mullen T. On decoding of rapid motor imagery in a diverse population using a high-density NIRS device. Front Neuroergon 2024; 5:1355534. [PMID: 38529269 PMCID: PMC10961353 DOI: 10.3389/fnrgo.2024.1355534] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Accepted: 02/20/2024] [Indexed: 03/27/2024]
Abstract
Introduction Functional near-infrared spectroscopy (fNIRS) aims to infer cognitive states such as the type of movement imagined by a study participant in a given trial using an optical method that can differentiate between oxygenation states of blood in the brain and thereby indirectly between neuronal activity levels. We present findings from an fNIRS study that aimed to test the applicability of a high-density (>3000 channels) NIRS device for use in short-duration (2 s) left/right hand motor imagery decoding in a diverse, but not explicitly balanced, subject population. A side aim was to assess relationships between data quality, self-reported demographic characteristics, and brain-computer interface (BCI) performance, with no subjects rejected from recruitment or analysis. Methods BCI performance was quantified using several published methods, including subject-specific and subject-independent approaches, along with a high-density fNIRS decoder previously validated in a separate study. Results We found that decoding of motor imagery on this population proved extremely challenging across all tested methods. Overall accuracy of the best-performing method (the high-density decoder) was 59.1 +/- 6.7% after excluding subjects where almost no optode-scalp contact was made over motor cortex and 54.7 +/- 7.6% when all recorded sessions were included. Deeper investigation revealed that signal quality, hemodynamic responses, and BCI performance were all strongly impacted by the hair phenotypical and demographic factors under investigation, with over half of variance in signal quality explained by demographic factors alone. Discussion Our results contribute to the literature reporting on challenges in using current-generation NIRS devices on subjects with long, dense, dark, and less pliable hair types along with the resulting potential for bias. Our findings confirm the need for increased focus on these populations, accurate reporting of data rejection choices across subject intake, curation, and final analysis in general, and signal a need for NIRS optode designs better optimized for the general population to facilitate more robust and inclusive research outcomes.
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13
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Yang J, Zhao S, Fu Z, Liu X. PMF-CNN: parallel multi-band fusion convolutional neural network for SSVEP-EEG decoding. Biomed Phys Eng Express 2024; 10:035002. [PMID: 38417170 DOI: 10.1088/2057-1976/ad2e36] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Accepted: 02/28/2024] [Indexed: 03/01/2024]
Abstract
Steady-state visual evoked potential (SSVEP) is a key technique of electroencephalography (EEG)-based brain-computer interfaces (BCI), which has been widely applied to neurological function assessment and postoperative rehabilitation. However, accurate decoding of the user's intended based on the SSVEP-EEG signals is challenging due to the low signal-to-noise ratio and large individual variability of the signals. To address these issues, we proposed a parallel multi-band fusion convolutional neural network (PMF-CNN). Multi frequency band signals were served as the input of PMF-CNN to fully utilize the time-frequency information of EEG. Three parallel modules, spatial self-attention (SAM), temporal self-attention (TAM), and squeeze-excitation (SEM), were proposed to automatically extract multi-dimensional features from spatial, temporal, and frequency domains, respectively. A novel spatial-temporal-frequency representation were designed to capture the correlation of electrode channels, time intervals, and different sub-harmonics by using SAM, TAM, and SEM, respectively. The three parallel modules operate independently and simultaneously. A four layers CNN classification module was designed to fuse parallel multi-dimensional features and achieve the accurate classification of SSVEP-EEG signals. The PMF-CNN was further interpreted by using brain functional connectivity analysis. The proposed method was validated using two large publicly available datasets. After trained using our proposed dual-stage training pattern, the classification accuracies were 99.37% and 93.96%, respectively, which are superior to the current state-of-the-art SSVEP-EEG classification algorithms. The algorithm exhibits high classification accuracy and good robustness, which has the potential to be applied to postoperative rehabilitation.
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Affiliation(s)
- Jianli Yang
- Key Laboratory of Digital Medical Engineering of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding 071002, People's Republic of China
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, People's Republic of China
| | - Songlei Zhao
- Key Laboratory of Digital Medical Engineering of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding 071002, People's Republic of China
| | - Zhiyu Fu
- Key Laboratory of Digital Medical Engineering of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding 071002, People's Republic of China
| | - Xiuling Liu
- Key Laboratory of Digital Medical Engineering of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding 071002, People's Republic of China
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14
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Reichert C, Sweeney-Reed CM, Hinrichs H, Dürschmid S. A toolbox for decoding BCI commands based on event-related potentials. Front Hum Neurosci 2024; 18:1358809. [PMID: 38505100 PMCID: PMC10949531 DOI: 10.3389/fnhum.2024.1358809] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Accepted: 02/14/2024] [Indexed: 03/21/2024] Open
Abstract
Commands in brain-computer interface (BCI) applications often rely on the decoding of event-related potentials (ERP). For instance, the P300 potential is frequently used as a marker of attention to an oddball event. Error-related potentials and the N2pc signal are further examples of ERPs used for BCI control. One challenge in decoding brain activity from the electroencephalogram (EEG) is the selection of the most suitable channels and appropriate features for a particular classification approach. Here we introduce a toolbox that enables ERP-based decoding using the full set of channels, while automatically extracting informative components from relevant channels. The strength of our approach is that it handles sequences of stimuli that encode multiple items using binary classification, such as target vs. nontarget events typically used in ERP-based spellers. We demonstrate examples of application scenarios and evaluate the performance of four openly available datasets: a P300-based matrix speller, a P300-based rapid serial visual presentation (RSVP) speller, a binary BCI based on the N2pc, and a dataset capturing error potentials. We show that our approach achieves performances comparable to those in the original papers, with the advantage that only conventional preprocessing is required by the user, while channel weighting and decoding algorithms are internally performed. Thus, we provide a tool to reliably decode ERPs for BCI use with minimal programming requirements.
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Affiliation(s)
- Christoph Reichert
- Department of Behavioral Neurology, Leibniz Institute for Neurobiology, Magdeburg, Germany
| | - Catherine M. Sweeney-Reed
- Neurocybernetics and Rehabilitation, Department of Neurology, Otto von Guericke University, Magdeburg, Germany
- Center for Behavioral Brain Sciences, Otto von Guericke University, Magdeburg, Germany
| | - Hermann Hinrichs
- Department of Behavioral Neurology, Leibniz Institute for Neurobiology, Magdeburg, Germany
- Center for Behavioral Brain Sciences, Otto von Guericke University, Magdeburg, Germany
- Department of Neurology, Otto von Guericke University, Magdeburg, Germany
| | - Stefan Dürschmid
- Department of Behavioral Neurology, Leibniz Institute for Neurobiology, Magdeburg, Germany
- Center for Behavioral Brain Sciences, Otto von Guericke University, Magdeburg, Germany
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, United States
- Department of Cellular Neuroscience, Leibniz Institute for Neurobiology, Magdeburg, Germany
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15
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Lv Z, Liu X, Dai M, Jin X, Huang X, Chen Z. Investigating critical brain area for EEG-based binocular color fusion and rivalry with EEGNet. Front Neurosci 2024; 18:1361486. [PMID: 38476872 PMCID: PMC10927996 DOI: 10.3389/fnins.2024.1361486] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2023] [Accepted: 02/14/2024] [Indexed: 03/14/2024] Open
Abstract
Introduction Binocular color fusion and rivalry are two specific phenomena in binocular vision, which could be used as experimental tools to study how the brain processes conflicting information. There is a lack of objective evaluation indexes to distinguish the fusion or rivalry for dichoptic color. Methods This paper introduced EEGNet to construct an EEG-based model for binocular color fusion and rivalry classification. We developed an EEG dataset from 10 subjects. Results By dividing the EEG data from five different brain areas to train the corresponding models, experimental results showed that: (1) the brain area represented by the back area had a large difference on EEG signals, the accuracy of model reached the highest of 81.98%, and more channels decreased the model performance; (2) there was a large effect of inter-subject variability, and the EEG-based recognition is still a very challenge across subjects; and (3) the statistics of EEG data are relatively stationary at different time for the same individual, the EEG-based recognition is highly reproducible for an individual. Discussion The critical channels for EEG-based binocular color fusion and rivalry could be meaningful for developing the brain computer interfaces (BCIs) based on color-related visual evoked potential (CVEP).
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Affiliation(s)
- Zhineng Lv
- School of Information Science and Technology, Yunnan Normal University, Kunming, China
- Engineering Research Center of Computer Vision and Intelligent Control Technology, Yunnan Provincial Department of Education, Kunming, China
- Yunnan Key Laboratory of Optoelectronic Information Technology, Kunming, China
| | - Xiang Liu
- Engineering Research Center of Computer Vision and Intelligent Control Technology, Yunnan Provincial Department of Education, Kunming, China
- Yunnan Key Laboratory of Optoelectronic Information Technology, Kunming, China
| | - Mengshi Dai
- Engineering Research Center of Computer Vision and Intelligent Control Technology, Yunnan Provincial Department of Education, Kunming, China
- Yunnan Key Laboratory of Optoelectronic Information Technology, Kunming, China
| | - Xuesong Jin
- Engineering Research Center of Computer Vision and Intelligent Control Technology, Yunnan Provincial Department of Education, Kunming, China
- Information Network Center, The Second People’s Hospital of Yuxi, Yuxi, China
| | - Xiaoqiao Huang
- Yunnan Key Laboratory of Optoelectronic Information Technology, Kunming, China
| | - Zaiqing Chen
- School of Information Science and Technology, Yunnan Normal University, Kunming, China
- Engineering Research Center of Computer Vision and Intelligent Control Technology, Yunnan Provincial Department of Education, Kunming, China
- Yunnan Key Laboratory of Optoelectronic Information Technology, Kunming, China
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16
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Wang X, Li B, Lin Y, Gao X. Multi-source domain adaptation based tempo-spatial convolution network for cross-subject EEG classification in RSVP task. J Neural Eng 2024; 21:016025. [PMID: 38324909 DOI: 10.1088/1741-2552/ad2710] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Accepted: 02/07/2024] [Indexed: 02/09/2024]
Abstract
Objective.Many subject-dependent methods were proposed for electroencephalogram (EEG) classification in rapid serial visual presentation (RSVP) task, which required a large amount of data from new subject and were time-consuming to calibrate system. Cross-subject classification can realize calibration reduction or zero calibration. However, cross-subject classification in RSVP task is still a challenge.Approach.This study proposed a multi-source domain adaptation based tempo-spatial convolution (MDA-TSC) network for cross-subject RSVP classification. The proposed network consisted of three modules. First, the common feature extraction with multi-scale tempo-spatial convolution was constructed to extract domain-invariant features across all subjects, which could improve generalization of the network. Second, the multi-branch domain-specific feature extraction and alignment was conducted to extract and align domain-specific feature distributions of source and target domains in pairs, which could consider feature distribution differences among source domains. Third, the domain-specific classifier was exploited to optimize the network through loss functions and obtain prediction for the target domain.Main results.The proposed network was evaluated on the benchmark RSVP dataset, and the cross-subject classification results showed that the proposed MDA-TSC network outperformed the reference methods. Moreover, the effectiveness of the MDA-TSC network was verified through both ablation studies and visualization.Significance.The proposed network could effectively improve cross-subject classification performance in RSVP task, and was helpful to reduce system calibration time.
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Affiliation(s)
- Xuepu Wang
- School of Integrated Circuits and Electronics, Beijing Institute of Technology, Beijing 100081, People's Republic of China
| | - Bowen Li
- School of Medicine, Tsinghua University, Beijing 100084, People's Republic of China
| | - Yanfei Lin
- School of Integrated Circuits and Electronics, Beijing Institute of Technology, Beijing 100081, People's Republic of China
| | - Xiaorong Gao
- School of Medicine, Tsinghua University, Beijing 100084, People's Republic of China
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17
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Yao Y, Stebner A, Tuytelaars T, Geirnaert S, Bertrand A. Identifying temporal correlations between natural single-shot videos and EEG signals. J Neural Eng 2024; 21:016018. [PMID: 38277701 DOI: 10.1088/1741-2552/ad2333] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Accepted: 01/26/2024] [Indexed: 01/28/2024]
Abstract
Objective.Electroencephalography (EEG) is a widely used technology for recording brain activity in brain-computer interface (BCI) research, where understanding the encoding-decoding relationship between stimuli and neural responses is a fundamental challenge. Recently, there is a growing interest in encoding-decoding natural stimuli in a single-trial setting, as opposed to traditional BCI literature where multi-trial presentations of synthetic stimuli are commonplace. While EEG responses to natural speech have been extensively studied, such stimulus-following EEG responses to natural video footage remain underexplored.Approach.We collect a new EEG dataset with subjects passively viewing a film clip and extract a few video features that have been found to be temporally correlated with EEG signals. However, our analysis reveals that these correlations are mainly driven by shot cuts in the video. To avoid the confounds related to shot cuts, we construct another EEG dataset with natural single-shot videos as stimuli and propose a new set of object-based features.Main results.We demonstrate that previous video features lack robustness in capturing the coupling with EEG signals in the absence of shot cuts, and that the proposed object-based features exhibit significantly higher correlations. Furthermore, we show that the correlations obtained with these proposed features are not dominantly driven by eye movements. Additionally, we quantitatively verify the superiority of the proposed features in a match-mismatch task. Finally, we evaluate to what extent these proposed features explain the variance in coherent stimulus responses across subjects.Significance.This work provides valuable insights into feature design for video-EEG analysis and paves the way for applications such as visual attention decoding.
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Affiliation(s)
- Yuanyuan Yao
- Department of Electrical Engineering, STADIUS, KU Leuven, Leuven, Belgium
| | - Axel Stebner
- Department of Electrical Engineering, PSI, KU Leuven, Leuven, Belgium
| | - Tinne Tuytelaars
- Department of Electrical Engineering, PSI, KU Leuven, Leuven, Belgium
| | - Simon Geirnaert
- Department of Electrical Engineering, STADIUS, Department of Neurosciences, ExpORL, KU Leuven, Leuven, Belgium
| | - Alexander Bertrand
- Department of Electrical Engineering, STADIUS, KU Leuven, Leuven, Belgium
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18
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Rassam R, Chen Q, Gai Y. Competing Visual Cues Revealed by Electroencephalography: Sensitivity to Motion Speed and Direction. Brain Sci 2024; 14:160. [PMID: 38391734 PMCID: PMC10886893 DOI: 10.3390/brainsci14020160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 01/25/2024] [Accepted: 02/02/2024] [Indexed: 02/24/2024] Open
Abstract
Motion speed and direction are two fundamental cues for the mammalian visual system. Neurons in various places of the neocortex show tuning properties in term of firing frequency to both speed and direction. The present study applied a 32-channel electroencephalograph (EEG) system to 13 human subjects while they were observing a single object moving with different speeds in various directions from the center of view to the periphery on a computer monitor. Depending on the experimental condition, the subjects were either required to fix their gaze at the center of the monitor while the object was moving or to track the movement with their gaze; eye-tracking glasses were used to ensure that they followed instructions. In each trial, motion speed and direction varied randomly and independently, forming two competing visual features. EEG signal classification was performed for each cue separately (e.g., 11 speed values or 11 directions), regardless of variations in the other cue. Under the eye-fixed condition, multiple subjects showed distinct preferences to motion direction over speed; however, two outliers showed superb sensitivity to speed. Under the eye-tracking condition, in which the EEG signals presumably contained ocular movement signals, all subjects showed predominantly better classification for motion direction. There was a trend that speed and direction were encoded by different electrode sites. Since EEG is a noninvasive and portable approach suitable for brain-computer interfaces (BCIs), this study provides insights on fundamental knowledge of the visual system as well as BCI applications based on visual stimulation.
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Affiliation(s)
- Rassam Rassam
- Biomedical Engineering, School of Science and Engineering, Saint Louis University, St. Louis, MO 63103, USA
| | - Qi Chen
- Biomedical Engineering, School of Science and Engineering, Saint Louis University, St. Louis, MO 63103, USA
| | - Yan Gai
- Biomedical Engineering, School of Science and Engineering, Saint Louis University, St. Louis, MO 63103, USA
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19
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Ning M, Duwadi S, Yücel MA, Von Lühmann A, Boas DA, Sen K. fNIRS Dataset During Complex Scene Analysis. bioRxiv 2024:2024.01.23.576715. [PMID: 38328139 PMCID: PMC10849700 DOI: 10.1101/2024.01.23.576715] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/09/2024]
Abstract
When analyzing complex scenes, humans often focus their attention on an object at a particular spatial location. The ability to decode the attended spatial location would facilitate brain computer interfaces for complex scene analysis (CSA). Here, we investigated capability of functional near-infrared spectroscopy (fNIRS) to decode audio-visual spatial attention in the presence of competing stimuli from multiple locations. We targeted dorsal frontoparietal network including frontal eye field (FEF) and intra-parietal sulcus (IPS) as well as superior temporal gyrus/planum temporal (STG/PT). They all were shown in previous functional magnetic resonance imaging (fMRI) studies to be activated by auditory, visual, or audio-visual spatial tasks. To date, fNIRS has not been applied to decode auditory and visual-spatial attention during CSA, and thus, no such dataset exists yet. This report provides an open-access fNIRS dataset that can be used to develop, test, and compare machine learning algorithms for classifying attended locations based on the fNIRS signals on a single trial basis.
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Affiliation(s)
- Matthew Ning
- Berenson-Allen Center for Noninvasive Brain Stimulation, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Sudan Duwadi
- Neurophotonics Center, Department of Biomedical Engineering, Boston University
| | - Meryem A. Yücel
- Neurophotonics Center, Department of Biomedical Engineering, Boston University
| | - Alexander Von Lühmann
- Neurophotonics Center, Department of Biomedical Engineering, Boston University
- BIFOLD – Berlin Institute for the Foundations of Learning and Data, 10587 Berlin, Germany
- Intelligent Biomedical Sensing (IBS) Lab, Technische Universität Berlin, 10587 Berlin, Germany
| | - David A. Boas
- Neurophotonics Center, Department of Biomedical Engineering, Boston University
| | - Kamal Sen
- Neurophotonics Center, Department of Biomedical Engineering, Boston University
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20
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Tai P, Ding P, Wang F, Gong A, Li T, Zhao L, Su L, Fu Y. Brain-computer interface paradigms and neural coding. Front Neurosci 2024; 17:1345961. [PMID: 38287988 PMCID: PMC10822902 DOI: 10.3389/fnins.2023.1345961] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Accepted: 12/28/2023] [Indexed: 01/31/2024] Open
Abstract
Brain signal patterns generated in the central nervous system of brain-computer interface (BCI) users are closely related to BCI paradigms and neural coding. In BCI systems, BCI paradigms and neural coding are critical elements for BCI research. However, so far there have been few references that clearly and systematically elaborated on the definition and design principles of the BCI paradigm as well as the definition and modeling principles of BCI neural coding. Therefore, these contents are expounded and the existing main BCI paradigms and neural coding are introduced in the review. Finally, the challenges and future research directions of BCI paradigm and neural coding were discussed, including user-centered design and evaluation for BCI paradigms and neural coding, revolutionizing the traditional BCI paradigms, breaking through the existing techniques for collecting brain signals and combining BCI technology with advanced AI technology to improve brain signal decoding performance. It is expected that the review will inspire innovative research and development of the BCI paradigm and neural coding.
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Affiliation(s)
- Pengrui Tai
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China
- Brain Cognition and Brain-Computer Intelligence Integration Group, Kunming University of Science and Technology, Kunming, China
| | - Peng Ding
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China
- Brain Cognition and Brain-Computer Intelligence Integration Group, Kunming University of Science and Technology, Kunming, China
| | - Fan Wang
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China
- Brain Cognition and Brain-Computer Intelligence Integration Group, Kunming University of Science and Technology, Kunming, China
| | - Anmin Gong
- School of Information Engineering, Chinese People’s Armed Police Force Engineering University, Xi’an, China
| | - Tianwen Li
- Brain Cognition and Brain-Computer Intelligence Integration Group, Kunming University of Science and Technology, Kunming, China
- Faculty of Science, Kunming University of Science and Technology, Kunming, China
| | - Lei Zhao
- Brain Cognition and Brain-Computer Intelligence Integration Group, Kunming University of Science and Technology, Kunming, China
- Faculty of Science, Kunming University of Science and Technology, Kunming, China
| | - Lei Su
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China
- Brain Cognition and Brain-Computer Intelligence Integration Group, Kunming University of Science and Technology, Kunming, China
| | - Yunfa Fu
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China
- Brain Cognition and Brain-Computer Intelligence Integration Group, Kunming University of Science and Technology, Kunming, China
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21
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Kimura R, Nambu I, Fujitsuka R, Maruyama Y, Yano S, Wada Y. An auditory brain-computer interface to detect changes in sound pressure level for automatic volume control. Heliyon 2024; 10:e23948. [PMID: 38223727 PMCID: PMC10784304 DOI: 10.1016/j.heliyon.2023.e23948] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Revised: 11/05/2023] [Accepted: 12/16/2023] [Indexed: 01/16/2024] Open
Abstract
Volume control is necessary to adjust sound levels for a comfortable audio or video listening experience. This study aims to develop an automatic volume control system based on a brain-computer interface (BCI). We thus focused on a BCI using an auditory oddball paradigm, and conducted two types of experiments. In the first experiment, the participant was asked to pay attention to a target sound where the sound level was high (70 dB) compared with the other sounds (60 dB). The brain activity measured by electroencephalogram showed large positive activity (P300) for the target sound, and classification of the target and nontarget sounds achieved an accuracy of 0.90. The second experiment adopted a two-target paradigm where a low sound level (50 dB) was introduced as the second target sound. P300 was also observed in the second experiment, and a value of 0.76 was obtained for the binary classification of the target and nontarget sounds. Further, we found that better accuracy was observed in large sound levels compared to small ones. These results suggest the possibility of using BCI for automatic volume control; however, it is necessary to improve its accuracy for application in daily life.
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Affiliation(s)
- Riki Kimura
- Graduate School of Engineering, Nagaoka University of Technology, 1603-1 Kamitomioka, Nagaoka, Niigata, 940-2188, Japan
| | - Isao Nambu
- Graduate School of Engineering, Nagaoka University of Technology, 1603-1 Kamitomioka, Nagaoka, Niigata, 940-2188, Japan
| | - Rui Fujitsuka
- Graduate School of Engineering, Nagaoka University of Technology, 1603-1 Kamitomioka, Nagaoka, Niigata, 940-2188, Japan
| | - Yoshiko Maruyama
- Department of Production Systems Engineering, National Institute of Technology, Hakodate College, 14-1 Tokura, Hakodate, Hokkaido, 042-8501, Japan
| | - Shohei Yano
- Department of Electrical and Electronic Systems Engineering, National Institute of Technology, Nagaoka College, 888 Nishikatakai, Nagaoka, Niigata, 940-8532, Japan
| | - Yasuhiro Wada
- Graduate School of Engineering, Nagaoka University of Technology, 1603-1 Kamitomioka, Nagaoka, Niigata, 940-2188, Japan
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Tucci DL. NIDCD's 5-year strategic plan seeks innovations in assistive device technologies. J Neural Eng 2024; 21:013002. [PMID: 38113536 PMCID: PMC10905644 DOI: 10.1088/1741-2552/ad171b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Accepted: 12/19/2023] [Indexed: 12/21/2023]
Affiliation(s)
- Debara L Tucci
- National Institute on Deafness and Other Communication Disorders, National Institutes of Health, Bethesda, MD, United States of America
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23
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Huang S, Paul U, Gupta S, Desai K, Guo M, Jung J, Capestany B, Krenzer WD, Stonecipher D, Farahany N. U.S. public perceptions of the sensitivity of brain data. J Law Biosci 2024; 11:lsad032. [PMID: 38259629 PMCID: PMC10800024 DOI: 10.1093/jlb/lsad032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
As we approach an era of potentially widespread consumer neurotechnology, scholars and organizations worldwide have started to raise concerns about the data privacy issues these devices will present. Notably absent in these discussions is empirical evidence about how the public perceives that same information. This article presents the results of a nationwide survey on public perceptions of brain data, to inform discussions of law and policy regarding brain data governance. The survey reveals that the public may perceive certain brain data as less sensitive than other 'private' information, like social security numbers, but more sensitive than some 'public' information, like media preferences. The findings also reveal that not all inferences about mental experiences may be perceived as equally sensitive, and perhaps not all data should be treated alike in ethical and policy discussions. An enhanced understanding of public perceptions of brain data could advance the development of ethical and legal norms concerning consumer neurotechnology.
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Affiliation(s)
- Shenyang Huang
- Center for Cognitive Neuroscience, Duke University, Durham, North Carolina, USA
- Duke Initiative for Science & Society, Durham, North Carolina, USA
| | - Umika Paul
- Duke Initiative for Science & Society, Durham, North Carolina, USA
- University of Massachusetts Chan Medical School, Worcester, Massachusetts, USA
| | - Shikhar Gupta
- Duke Initiative for Science & Society, Durham, North Carolina, USA
| | - Karen Desai
- Duke Initiative for Science & Society, Durham, North Carolina, USA
- University of Michigan Medical School, Ann Arbor, Michigan, USA
| | - Melinda Guo
- Duke Initiative for Science & Society, Durham, North Carolina, USA
| | - Jennifer Jung
- University of Massachusetts Chan Medical School, Worcester, Massachusetts, USA
| | | | | | - Dylan Stonecipher
- Duke Initiative for Science & Society, Durham, North Carolina, USA
- Duke University, Durham, North Carolina, USA
| | - Nita Farahany
- Duke Initiative for Science & Society, Durham, North Carolina, USA
- Duke University, Durham, North Carolina, USA
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Várkuti B, Halász L, Hagh Gooie S, Miklós G, Smits Serena R, van Elswijk G, McIntyre CC, Lempka SF, Lozano AM, Erōss L. Conversion of a medical implant into a versatile computer-brain interface. Brain Stimul 2024; 17:39-48. [PMID: 38145752 DOI: 10.1016/j.brs.2023.12.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Revised: 12/19/2023] [Accepted: 12/20/2023] [Indexed: 12/27/2023] Open
Abstract
BACKGROUND Information transmission into the human nervous system is the basis for a variety of prosthetic applications. Spinal cord stimulation (SCS) systems are widely available, have a well documented safety record, can be implanted minimally invasively, and are known to stimulate afferent pathways. Nonetheless, SCS devices are not yet used for computer-brain-interfacing applications. OBJECTIVE Here we aimed to establish computer-to-brain communication via medical SCS implants in a group of 20 individuals who had been operated for the treatment of chronic neuropathic pain. METHODS In the initial phase, we conducted interface calibration with the aim of determining personalized stimulation settings that yielded distinct and reproducible sensations. These settings were subsequently utilized to generate inputs for a range of behavioral tasks. We evaluated the required calibration time, task training duration, and the subsequent performance in each task. RESULTS We could establish a stable spinal computer-brain interface in 18 of the 20 participants. Each of the 18 then performed one or more of the following tasks: A rhythm-discrimination task (n = 13), a Morse-decoding task (n = 3), and/or two different balance/body-posture tasks (n = 18; n = 5). The median calibration time was 79 min. The median training time for learning to use the interface in a subsequent task was 1:40 min. In each task, every participant demonstrated successful performance, surpassing chance levels. CONCLUSION The results constitute the first proof-of-concept of a general purpose computer-brain interface paradigm that could be deployed on present-day medical SCS platforms.
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Affiliation(s)
| | - László Halász
- Albert-Szentgyörgyi Medical School, Doctoral School of Clinical Medicine, Clinical and Experimental Research for Reconstructive and Organ-Sparing Surgery, University of Szeged, Szeged, Hungary
| | | | - Gabriella Miklós
- CereGate GmbH, München, Germany; National Institute of Mental Health, Neurology, and Neurosurgery, Budapest, Hungary; János Szentágothai Doctoral School of Neurosciences, Semmelweis University, Budapest, Hungary
| | - Ricardo Smits Serena
- CereGate GmbH, München, Germany; Department of Orthopaedics and Sports Orthopaedics, Klinikum Rechts der Isar, Technical University of Munich, München, Germany
| | | | - Cameron C McIntyre
- Department of Biomedical Engineering and Department of Neurosurgery, Duke University, Durham, NC, USA
| | - Scott F Lempka
- Department of Biomedical Engineering, Department of Anesthesiology and the Biointerfaces Institute, University of Michigan, Ann Arbor, MI, USA
| | - Andres M Lozano
- Division of Neurosurgery, Department of Surgery, University of Toronto, Toronto, Ontario, Canada
| | - Loránd Erōss
- National Institute of Mental Health, Neurology, and Neurosurgery, Budapest, Hungary
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25
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Moreno-Alcayde Y, Traver VJ, Leiva LA. Sneaky emotions: impact of data partitions in affective computing experiments with brain-computer interfacing. Biomed Eng Lett 2024; 14:103-113. [PMID: 38186953 PMCID: PMC10769959 DOI: 10.1007/s13534-023-00316-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 08/07/2023] [Accepted: 08/22/2023] [Indexed: 01/09/2024] Open
Abstract
Brain-Computer Interfacing (BCI) has shown promise in Machine Learning (ML) for emotion recognition. Unfortunately, how data are partitioned in training/test splits is often overlooked, which makes it difficult to attribute research findings to actual modeling improvements or to partitioning issues. We introduce the "data transfer rate" construct (i.e., how much data of the test samples are seen during training) and use it to examine data partitioning effects under several conditions. As a use case, we consider emotion recognition in videos using electroencephalogram (EEG) signals. Three data splits are considered, each representing a relevant BCI task: subject-independent (affective decoding), video-independent (affective annotation), and time-based (feature extraction). Model performance may change significantly (ranging e.g. from 50% to 90%) depending on how data is partitioned, in classification accuracy. This was evidenced in all experimental conditions tested. Our results show that (1) for affective decoding, it is hard to achieve performance above the baseline case (random classification) unless some data of the test subjects are considered in the training partition; (2) for affective annotation, having data from the same subject in training and test partitions, even though they correspond to different videos, also increases performance; and (3) later signal segments are generally more discriminative, but it is the number of segments (data points) what matters the most. Our findings not only have implications in how brain data are managed, but also in how experimental conditions and results are reported.
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Affiliation(s)
- Yoelvis Moreno-Alcayde
- Institute of New Imaging Technologies, Universitat Jaume I, Av. Vicent Sos Baynat, s/n, Castellón, 12071 Castellón Spain
| | - V. Javier Traver
- Institute of New Imaging Technologies, Universitat Jaume I, Av. Vicent Sos Baynat, s/n, Castellón, 12071 Castellón Spain
| | - Luis A. Leiva
- University of Luxembourg, Esch-sur-Alzette, Luxembourg
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Rodríguez-Azar PI, Mejía-Muñoz JM, Cruz-Mejía O, Torres-Escobar R, López LVR. Fog Computing for Control of Cyber-Physical Systems in Industry Using BCI. Sensors (Basel) 2023; 24:149. [PMID: 38203012 PMCID: PMC10781321 DOI: 10.3390/s24010149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Revised: 12/23/2023] [Accepted: 12/25/2023] [Indexed: 01/12/2024]
Abstract
Brain-computer interfaces use signals from the brain, such as EEG, to determine brain states, which in turn can be used to issue commands, for example, to control industrial machinery. While Cloud computing can aid in the creation and operation of industrial multi-user BCI systems, the vast amount of data generated from EEG signals can lead to slow response time and bandwidth problems. Fog computing reduces latency in high-demand computation networks. Hence, this paper introduces a fog computing solution for BCI processing. The solution consists in using fog nodes that incorporate machine learning algorithms to convert EEG signals into commands to control a cyber-physical system. The machine learning module uses a deep learning encoder to generate feature images from EEG signals that are subsequently classified into commands by a random forest. The classification scheme is compared using various classifiers, being the random forest the one that obtained the best performance. Additionally, a comparison was made between the fog computing approach and using only cloud computing through the use of a fog computing simulator. The results indicate that the fog computing method resulted in less latency compared to the solely cloud computing approach.
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Affiliation(s)
- Paula Ivone Rodríguez-Azar
- Departamento de Ingeniería Industrial y Manufactura, Instituto de Ingeniería y Tecnología, Universidad Autónoma de Ciudad Juárez, Ciudad Juárez 32310, Mexico
| | - Jose Manuel Mejía-Muñoz
- Departamento de Ingeniería Eléctrica, Instituto de Ingenieria y Tecnologia, Universidad Autónoma de Ciudad Juárez, Ciudad Juárez 32310, Mexico;
| | - Oliverio Cruz-Mejía
- Departamento de Ingeniería Industrial, FES Aragón, Universidad Nacional Autónoma de México, Mexico 57171, Mexico;
| | | | - Lucero Verónica Ruelas López
- Departamento de Ingeniería Eléctrica, Instituto de Ingenieria y Tecnologia, Universidad Autónoma de Ciudad Juárez, Ciudad Juárez 32310, Mexico;
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LaRocco J, Tahmina Q, Lecian S, Moore J, Helbig C, Gupta S. Evaluation of an English language phoneme-based imagined speech brain computer interface with low-cost electroencephalography. Front Neuroinform 2023; 17:1306277. [PMID: 38192730 PMCID: PMC10773705 DOI: 10.3389/fninf.2023.1306277] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Accepted: 11/30/2023] [Indexed: 01/10/2024] Open
Abstract
Introduction Paralyzed and physically impaired patients face communication difficulties, even when they are mentally coherent and aware. Electroencephalographic (EEG) brain-computer interfaces (BCIs) offer a potential communication method for these people without invasive surgery or physical device controls. Methods Although virtual keyboard protocols are well documented in EEG BCI paradigms, these implementations are visually taxing and fatiguing. All English words combine 44 unique phonemes, each corresponding to a unique EEG pattern. In this study, a complete phoneme-based imagined speech EEG BCI was developed and tested on 16 subjects. Results Using open-source hardware and software, machine learning models, such as k-nearest neighbor (KNN), reliably achieved a mean accuracy of 97 ± 0.001%, a mean F1 of 0.55 ± 0.01, and a mean AUC-ROC of 0.68 ± 0.002 in a modified one-versus-rest configuration, resulting in an information transfer rate of 304.15 bits per minute. In line with prior literature, the distinguishing feature between phonemes was the gamma power on channels F3 and F7. Discussion However, adjustments to feature selection, trial window length, and classifier algorithms may improve performance. In summary, these are iterative changes to a viable method directly deployable in current, commercially available systems and software. The development of an intuitive phoneme-based EEG BCI with open-source hardware and software demonstrates the potential ease with which the technology could be deployed in real-world applications.
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Affiliation(s)
- John LaRocco
- Wexner Medical Center, The Ohio State University, Columbus, OH, United States
| | - Qudsia Tahmina
- Department of Electrical Engineering, The Ohio State University, Columbus, OH, United States
| | - Sam Lecian
- Department of Electrical Engineering, The Ohio State University, Columbus, OH, United States
| | - Jason Moore
- Department of Electrical Engineering, The Ohio State University, Columbus, OH, United States
| | - Cole Helbig
- Department of Electrical Engineering, The Ohio State University, Columbus, OH, United States
| | - Surya Gupta
- Department of Electrical Engineering, The Ohio State University, Columbus, OH, United States
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Alder G, Taylor D, Rashid U, Olsen S, Brooks T, Terry G, Niazi IK, Signal N. A Brain Computer Interface Neuromodulatory Device for Stroke Rehabilitation: Iterative User-Centered Design Approach. JMIR Rehabil Assist Technol 2023; 10:e49702. [PMID: 38079202 PMCID: PMC10750233 DOI: 10.2196/49702] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Revised: 09/03/2023] [Accepted: 09/27/2023] [Indexed: 12/28/2023] Open
Abstract
BACKGROUND Rehabilitation technologies for people with stroke are rapidly evolving. These technologies have the potential to support higher volumes of rehabilitation to improve outcomes for people with stroke. Despite growing evidence of their efficacy, there is a lack of uptake and sustained use in stroke rehabilitation and a call for user-centered design approaches during technology design and development. This study focuses on a novel rehabilitation technology called exciteBCI, a complex neuromodulatory wearable technology in the prototype stage that augments locomotor rehabilitation for people with stroke. The exciteBCI consists of a brain computer interface, a muscle electrical stimulator, and a mobile app. OBJECTIVE This study presents the evaluation phase of an iterative user-centered design approach supported by a qualitative descriptive methodology that sought to (1) explore users' perspectives and experiences of exciteBCI and how well it fits with rehabilitation, and (2) facilitate modifications to exciteBCI design features. METHODS The iterative usability evaluation of exciteBCI was conducted in 2 phases. Phase 1 consisted of 3 sprint cycles consisting of single usability sessions with people with stroke (n=4) and physiotherapists (n=4). During their interactions with exciteBCI, participants used a "think-aloud" approach, followed by a semistructured interview. At the end of each sprint cycle, device requirements were gathered and the device was modified in preparation for the next cycle. Phase 2 focused on a "near-live" approach in which 2 people with stroke and 1 physiotherapist participated in a 3-week program of rehabilitation augmented by exciteBCI (n=3). Participants completed a semistructured interview at the end of the program. Data were analyzed from both phases using conventional content analysis. RESULTS Overall, participants perceived and experienced exciteBCI positively, while providing guidance for iterative changes. Five interrelated themes were identified from the data: (1) "This is rehab" illustrated that participants viewed exciteBCI as having a good fit with rehabilitation practice; (2) "Getting the most out of rehab" highlighted that exciteBCI was perceived as a means to enhance rehabilitation through increased engagement and challenge; (3) "It is a tool not a therapist," revealed views that the technology could either enhance or disrupt the therapeutic relationship; and (4) "Weighing up the benefits versus the burden" and (5) "Don't make me look different" emphasized important design considerations related to device set-up, use, and social acceptability. CONCLUSIONS This study offers several important findings that can inform the design and implementation of rehabilitation technologies. These include (1) the design of rehabilitation technology should support the therapeutic relationship between the patient and therapist, (2) social acceptability is a design priority in rehabilitation technology but its importance varies depending on the use context, and (3) there is value in using design research methods that support understanding usability in the context of sustained use.
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Affiliation(s)
- Gemma Alder
- Rehabilitation Innovation Centre, Health and Rehabilitation Research Institute, Auckland University of Technology, Auckland, New Zealand
| | - Denise Taylor
- Rehabilitation Innovation Centre, Health and Rehabilitation Research Institute, Auckland University of Technology, Auckland, New Zealand
| | - Usman Rashid
- Rehabilitation Innovation Centre, Health and Rehabilitation Research Institute, Auckland University of Technology, Auckland, New Zealand
| | - Sharon Olsen
- Rehabilitation Innovation Centre, Health and Rehabilitation Research Institute, Auckland University of Technology, Auckland, New Zealand
| | - Thonia Brooks
- Rehabilitation Innovation Centre, Health and Rehabilitation Research Institute, Auckland University of Technology, Auckland, New Zealand
| | - Gareth Terry
- Rehabilitation Innovation Centre, Health and Rehabilitation Research Institute, Auckland University of Technology, Auckland, New Zealand
| | - Imran Khan Niazi
- Rehabilitation Innovation Centre, Health and Rehabilitation Research Institute, Auckland University of Technology, Auckland, New Zealand
- Centre for Chiropractic Research, New Zealand College of Chiropractic, Auckland, New Zealand
- Sensory Motor Integration, Department of Health Science and Technology, Aalborg University, Aalborg, Denmark
| | - Nada Signal
- Rehabilitation Innovation Centre, Health and Rehabilitation Research Institute, Auckland University of Technology, Auckland, New Zealand
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29
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He R. Perspective of Signal Processing-Based on Brain-Computer Interfaces Using Machine Learning Methods. Stud Health Technol Inform 2023; 308:295-302. [PMID: 38007753 DOI: 10.3233/shti230853] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2023]
Abstract
The application of artificial intelligence (AI) algorithms is an indispensable portion of developing brain-computer interfaces (BCI). With the continuous development of AI concepts and related technologies. AI algorithms such as neural networks play an increasingly powerful and extensive role in brain-computer interfaces. However, brain-computer interfaces are still facing many technical challenges. Due to the limitations of AI algorithms, brain-computer interfaces not only work with limited accuracy, but also can only be applied to certain simple scenarios. In order to explore the future directions and improvements of AI algorithms in the area of brain-computer interfaces, this paper will review and analyse the advanced applications of AI algorithms in the field of brain-computer interfaces in recent years and give possible future enhancements and development directions for the controversial parts of them. This review first presents the effects of different AI algorithms in BCI applications. A multi-objective classification method is compared with evolutionary algorithms in feature extraction of data. Then, a kind of supervised learning algorithm based on Event Related Potential (ERP) tags is presented to achieve a high accuracy in the process of pattern recognition. Finally, as an important experimental paradigm for BCI, a combined TFD-PSR-CSP feature extraction method, is explained for the problem of motor imagery. The "Discussion" part comprehensively analyses the advantages and disadvantages of the above algorithms and proposes a deep learning-based artificial intelligence algorithm in order to solve the problems arising from the above algorithms.
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Affiliation(s)
- Ruyue He
- Department of Electronics and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China
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30
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Wolf P, Götzelmann T. VEPdgets: Towards Richer Interaction Elements Based on Visually Evoked Potentials. Sensors (Basel) 2023; 23:9127. [PMID: 38005515 PMCID: PMC10674685 DOI: 10.3390/s23229127] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Revised: 11/06/2023] [Accepted: 11/08/2023] [Indexed: 11/26/2023]
Abstract
For brain-computer interfaces, a variety of technologies and applications already exist. However, current approaches use visual evoked potentials (VEP) only as action triggers or in combination with other input technologies. This paper shows that the losing visually evoked potentials after looking away from a stimulus is a reliable temporal parameter. The associated latency can be used to control time-varying variables using the VEP. In this context, we introduced VEP interaction elements (VEP widgets) for a value input of numbers, which can be applied in various ways and is purely based on VEP technology. We carried out a user study in a desktop as well as in a virtual reality setting. The results for both settings showed that the temporal control approach using latency correction could be applied to the input of values using the proposed VEP widgets. Even though value input is not very accurate under untrained conditions, users could input numerical values. Our concept of applying latency correction to VEP widgets is not limited to the input of numbers.
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Affiliation(s)
| | - Timo Götzelmann
- Nuremberg Institute of Technology, Chair of Ambient Intelligence, D-90489 Nuremberg, Germany
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31
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Zapała D, Augustynowicz P, Tokovarov M, Iwanowicz P, Droździel P. Brief Visual Deprivation Effects on Brain Oscillations During Kinesthetic and Visual-motor Imagery. Neuroscience 2023; 532:37-49. [PMID: 37625688 DOI: 10.1016/j.neuroscience.2023.08.022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Revised: 08/10/2023] [Accepted: 08/14/2023] [Indexed: 08/27/2023]
Abstract
It is widely recognized that opening and closing the eyes can direct attention to external or internal stimuli processing. This has been confirmed by studies showing the effects of changes in visual stimulation changes on cerebral activity during different tasks, e.g., motor imagery and execution. However, an essential aspect of creating a mental representation of motion, such as imagery perspective, has not yet been investigated in the present context. Our study aimed to verify the effect of brief visual deprivation (under eyes open [EO] and eyes closed [EC] conditions) on brain wave oscillations and behavioral performance during kinesthetic imagery (KMI) and visual-motor imagery (VMI) tasks. We focused on the alpha and beta rhythms from visual- and motor-related EEG activity sources. Additionally, we used machine learning algorithms to establish whether the registered differences in brain oscillations might affect motor imagery brain-computer interface (MI-BCI) performance. The results showed that the occipital areas in the EC condition presented significantly stronger desynchronization during VMI tasks, which is typical for enhanced visual stimuli processing. Furthermore, the stronger desynchronization of alpha rhythms from motor areas in the EO, than EC condition confirmed previous effects obtained during real movements. It was also found that simulating movement under EC/EO conditions affected signal classification accuracy, which has practical implications for MI-BCI effectiveness. These findings suggest that shifting processing toward external or internal stimuli modulates brain rhythm oscillations associated with different perspectives on the mental representation of movement.
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Affiliation(s)
- Dariusz Zapała
- Institute of Psychology, Department of Experimental Psychology, The John Paul II Catholic University of Lublin, 20950 Lublin, Poland.
| | - Paweł Augustynowicz
- Institute of Psychology, Department of Experimental Psychology, The John Paul II Catholic University of Lublin, 20950 Lublin, Poland.
| | | | - Paulina Iwanowicz
- Institute of Psychology, Department of Experimental Psychology, The John Paul II Catholic University of Lublin, 20950 Lublin, Poland.
| | - Paulina Droździel
- Institute of Psychology, Department of Experimental Psychology, The John Paul II Catholic University of Lublin, 20950 Lublin, Poland.
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32
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Fleury M, Figueiredo P, Vourvopoulos A, Lécuyer A. Two is better? combining EEG and fMRI for BCI and neurofeedback: a systematic review. J Neural Eng 2023; 20:051003. [PMID: 37879343 DOI: 10.1088/1741-2552/ad06e1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Accepted: 10/25/2023] [Indexed: 10/27/2023]
Abstract
Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) are two commonly used non-invasive techniques for measuring brain activity in neuroscience and brain-computer interfaces (BCI).Objective. In this review, we focus on the use of EEG and fMRI in neurofeedback (NF) and discuss the challenges of combining the two modalities to improve understanding of brain activity and achieve more effective clinical outcomes. Advanced technologies have been developed to simultaneously record EEG and fMRI signals to provide a better understanding of the relationship between the two modalities. However, the complexity of brain processes and the heterogeneous nature of EEG and fMRI present challenges in extracting useful information from the combined data.Approach. We will survey existing EEG-fMRI combinations and recent studies that exploit EEG-fMRI in NF, highlighting the experimental and technical challenges.Main results. We made a classification of the different combination of EEG-fMRI for NF, we provide a review of multimodal analysis methods for EEG-fMRI features. We also survey the current state of research on EEG-fMRI in the different existing NF paradigms. Finally, we also identify some of the remaining challenges in this field.Significance. By exploring EEG-fMRI combinations in NF, we are advancing our knowledge of brain function and its applications in clinical settings. As such, this review serves as a valuable resource for researchers, clinicians, and engineers working in the field of neural engineering and rehabilitation, highlighting the promising future of EEG-fMRI-based NF.
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Affiliation(s)
- Mathis Fleury
- Univ Rennes, Inria, CNRS, Inserm, Empenn ERL U1228 Rennes, France
- ISR-Lisboa/LARSyS and Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
| | - Patrícia Figueiredo
- ISR-Lisboa/LARSyS and Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
| | - Athanasios Vourvopoulos
- ISR-Lisboa/LARSyS and Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
| | - Anatole Lécuyer
- Univ Rennes, Inria, CNRS, Inserm, Empenn ERL U1228 Rennes, France
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Knudson D. A tale of two instructional experiences: student engagement in active learning and emergency remote learning of biomechanics. Sports Biomech 2023; 22:1485-1495. [PMID: 32924795 DOI: 10.1080/14763141.2020.1810306] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/29/2020] [Accepted: 08/11/2020] [Indexed: 10/23/2022]
Abstract
This study documents student engagement in face-to-face low-tech active learning and student perceptions of emergency remote instruction due to the COVID-19 pandemic in introductory biomechanics. Students in two classes received 8 weeks of face-to-face instruction with five low-tech active learning techniques and then received 6 weeks of emergency remote, online instruction. Learning was measured using pre-test and post-test administrations of the biomechanics concept inventory (BCI). A survey of engagement in active learning with additional questions on active learning and online instruction were collected with the post-test. No student perceptions of engagement in active learning or online instruction were correlated with learning measured by normalised gain. Student's perception of the 'value of group activity' factor from survey was significantly correlated (r2 = 12%) with the number of students typically in active learning groups. There was a significant correlation (r2 = 46%) between student perception of reading the textbook before online video lessons and perception of value of the video lessons in the online portion of the course. Most students (59%) preferred face-to-face instruction in biomechanics. While up to 28% of students may have reported resistance to group-based active learning, low-tech active learning significantly improved mastery of biomechanics concepts above levels previously reported for lecture alone.
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Affiliation(s)
- Duane Knudson
- Department of Health and Human Performance, Texas State University, San Marcos, TX, USA
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34
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Sankaran N, Moses D, Chiong W, Chang EF. Recommendations for promoting user agency in the design of speech neuroprostheses. Front Hum Neurosci 2023; 17:1298129. [PMID: 37920562 PMCID: PMC10619159 DOI: 10.3389/fnhum.2023.1298129] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Accepted: 10/04/2023] [Indexed: 11/04/2023] Open
Abstract
Brain-computer interfaces (BCI) that directly decode speech from brain activity aim to restore communication in people with paralysis who cannot speak. Despite recent advances, neural inference of speech remains imperfect, limiting the ability for speech BCIs to enable experiences such as fluent conversation that promote agency - that is, the ability for users to author and transmit messages enacting their intentions. Here, we make recommendations for promoting agency based on existing and emerging strategies in neural engineering. The focus is on achieving fast, accurate, and reliable performance while ensuring volitional control over when a decoder is engaged, what exactly is decoded, and how messages are expressed. Additionally, alongside neuroscientific progress within controlled experimental settings, we argue that a parallel line of research must consider how to translate experimental successes into real-world environments. While such research will ultimately require input from prospective users, here we identify and describe design choices inspired by human-factors work conducted in existing fields of assistive technology, which address practical issues likely to emerge in future real-world speech BCI applications.
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Affiliation(s)
- Narayan Sankaran
- Kavli Center for Ethics, Science and the Public, University of California, Berkeley, Berkeley, CA, United States
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, United States
- Weill Institute for Neuroscience, University of California, San Francisco, San Francisco, CA, United States
| | - David Moses
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, United States
- Weill Institute for Neuroscience, University of California, San Francisco, San Francisco, CA, United States
| | - Winston Chiong
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, San Francisco, CA, United States
| | - Edward F. Chang
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, United States
- Weill Institute for Neuroscience, University of California, San Francisco, San Francisco, CA, United States
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Sebastián-Romagosa M, Cho W, Ortner R, Sieghartsleitner S, Von Oertzen TJ, Kamada K, Laureys S, Allison BZ, Guger C. Brain-computer interface treatment for gait rehabilitation in stroke patients. Front Neurosci 2023; 17:1256077. [PMID: 37920297 PMCID: PMC10618349 DOI: 10.3389/fnins.2023.1256077] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Accepted: 09/28/2023] [Indexed: 11/04/2023] Open
Abstract
The use of Brain-Computer Interfaces (BCI) as rehabilitation tools for chronically ill neurological patients has become more widespread. BCIs combined with other techniques allow the user to restore neurological function by inducing neuroplasticity through real-time detection of motor-imagery (MI) as patients perform therapy tasks. Twenty-five stroke patients with gait disability were recruited for this study. Participants performed 25 sessions with the MI-BCI and assessment visits to track functional changes during the therapy. The results of this study demonstrated a clinically significant increase in walking speed of 0.19 m/s, 95%CI [0.13-0.25], p < 0.001. Patients also reduced spasticity and improved their range of motion and muscle contraction. The BCI treatment was effective in promoting long-lasting functional improvements in the gait speed of chronic stroke survivors. Patients have more movements in the lower limb; therefore, they can walk better and safer. This functional improvement can be explained by improved neuroplasticity in the central nervous system.
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Affiliation(s)
| | - Woosang Cho
- g.tec Medical Engineering GmbH, Schiedlberg, Austria
| | - Rupert Ortner
- g.tec Medical Engineering Spain SL, Barcelona, Catalonia, Spain
| | | | | | - Kyousuke Kamada
- Department for Neurosurgery, Asahikawa Medical University, Asahikawa, Japan
- Hokashin Group Megumino Hospital, Sapporo, Japan
| | - Steven Laureys
- Coma Science Group, GIGA Consciousness Research Unit, University and University Hospital of Liège, Liège, Belgium
- CERVO Brain Research Center, Laval University, Québec, QC, Canada
- Consciousness Science Institute, Hangzhou Normal University, Hangzhou, Zhejiang, China
| | - Brendan Z. Allison
- Department of Cognitive Science, University of California, San Diego, La Jolla, CA, United States
| | - Christoph Guger
- g.tec Medical Engineering Spain SL, Barcelona, Catalonia, Spain
- g.tec Medical Engineering GmbH, Schiedlberg, Austria
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Rainey S. A gap between reasons for skilled use of BCI speech devices and reasons for utterances, with implications for speech ownership. Front Hum Neurosci 2023; 17:1248806. [PMID: 37915755 PMCID: PMC10616452 DOI: 10.3389/fnhum.2023.1248806] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Accepted: 09/11/2023] [Indexed: 11/03/2023] Open
Abstract
The skilled use of a speech BCI device will draw upon practical experience gained through the use of that very device. The reasons a user may have for using a device in a particular way, reflecting that skill gained via familiarity with the device, may differ significantly from the reasons that a speaker might have for their utterances. The potential divergence between reasons constituting skilled use and BCI-mediated speech output may serve to make clear an instrumental relationship between speaker and BCI speech device. This will affect the way in which the device and the speech it produces for the user can be thought of as being "reasons responsive", hence the way in which the user can be said to be in control of their device. Ultimately, this divergence will come down to how ownership of produced speech can be considered. The upshot will be that skillful use of a synthetic speech device might include practices that diverge from standard speech in significant ways. This might further indicate that synthetic speech devices ought to be considered as different from, not continuous with, standard speech.
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Affiliation(s)
- Stephen Rainey
- Ethics and Philosophy of Technology, Delft University of Technology, Delft, Netherlands
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Mijani A, Cherloo MN, Tang H, Zhan L. Spectrum-Enhanced TRCA (SE-TRCA): A novel approach for direction detection in SSVEP-based BCI. Comput Biol Med 2023; 166:107488. [PMID: 37778215 DOI: 10.1016/j.compbiomed.2023.107488] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Revised: 08/26/2023] [Accepted: 09/15/2023] [Indexed: 10/03/2023]
Abstract
The Steady State Visual Evoked Potential (SSVEP) is a widely used component in BCIs due to its high noise resistance and low equipment requirements. Recently, a novel SSVEP-based paradigm has been introduced for direction detection, in which, unlike the common SSVEP paradigms that use several frequency stimuli, only one flickering stimulus is used and it makes direction detection very challenging. So far, only the CCA method has been used for direction detection using SSVEP component analysis. Since Canonical Correlation Analysis (CCA) has some limitations, a Task-Related Component Analysis (TRCA) based method has been introduced for feature extraction to improve the direction detection performance. Although these methods have been proven efficient, they do not utilize the latent frequency information in the EEG signal. Therefore, the performance of direction detection using SSVEP component analysis is still suboptimal. For further improvement, the TRCA-based algorithm is enhanced by incorporating frequency information and introducing Spectrum-Enhanced TRCA (SE-TRCA). SE-TRCA method can utilize frequency information in conjunction with spatial information by concatenating the EEG signal and its shifted version. Accordingly, the obtained spatio-spectral filters perform as a Finite Impulse Response (FIR) filter. To evaluate the proposed SE-TRCA method, two different sorts of datasets (1) a hybrid BCI dataset (including SSVEP component for direction detection) and (2) a pure benchmark SSVEP dataset (including SSVEP component for frequency detection) have been used. Our experiments showed that the accuracy of direction detection using the proposed SE-TRCA and TRCA approaches compared to CCA-based approach have been increased by 23.35% and 28.24%, respectively. Furthermore, the accuracy of character recognition obtained from integrating P300 and SSVEP components in CCA, TRCA, and SETRCA approaches are 54.01%, 56.02%, and 58.56%, on the hybrid dataset, respectively. The evaluation of the SE-TRCA method on the benchmark SSVEP dataset demonstrates that the SE-TRCA method outperforms both CCA and TRCA, particularly regarding frequency detection accuracy. In this specific dataset, the SE-TRCA method achieved an impressive frequency detection accuracy of 98.19% for a 3-s signal, surpassing the accuracies of TRCA and CCA, which were 97.91% and 90.47%, respectively. These results demonstrated that the TRCA-based approach is more efficient than the CCA approach to extracting spatial filters. Moreover, SE-TRCA, extracting both Spectral and spatial information from the EEG signal, can capture more discriminative features from the SSVEP component and increase the accuracy of classification. The results of this study emphasize the effectiveness of the proposed SE-TRCA approach across different SSVEP paradigms and tasks. These findings provide strong evidence for the method's ability to generalize well in SSVEP analysis.
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Affiliation(s)
- AmirMohammad Mijani
- Department of Electrical and Computer Engineering, University of Pittsburgh, 3700 O'Hara Street, Pittsburgh, 15261, PA, USA.
| | - Mohammad Norizadeh Cherloo
- Department of Biomedical Engineering, University of Science and Technology (IUST), Narmak, Tehran, 16846-13114, Tehran, Iran.
| | - Haoteng Tang
- Department of Computer Science, University of Texas Rio Grande Valley, 1201 W University Dr, Edinburg, 78539, TX, USA.
| | - Liang Zhan
- Department of Electrical and Computer Engineering, University of Pittsburgh, 3700 O'Hara Street, Pittsburgh, 15261, PA, USA.
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Marciniak B, Kciuk M, Mujwar S, Sundaraj R, Bukowski K, Gruszka R. In Vitro and In Silico Investigation of BCI Anticancer Properties and Its Potential for Chemotherapy-Combined Treatments. Cancers (Basel) 2023; 15:4442. [PMID: 37760412 PMCID: PMC10526149 DOI: 10.3390/cancers15184442] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Revised: 08/10/2023] [Accepted: 08/30/2023] [Indexed: 09/29/2023] Open
Abstract
BACKGROUND DUSP6 phosphatase serves as a negative regulator of MAPK kinases involved in numerous cellular processes. BCI has been identified as a potential allosteric inhibitor with anticancer activity. Our study was designed to test the anticancer properties of BCI in colon cancer cells, to characterize the effect of this compound on chemotherapeutics such as irinotecan and oxaliplatin activity, and to identify potential molecular targets for this inhibitor. METHODS BCI cytotoxicity, proapoptotic activity, and cell cycle distribution were investigated in vitro on three colon cancer cell lines (DLD1, HT-29, and Caco-2). In silico investigation was prepared to assess BCI drug-likeness and identify potential molecular targets. RESULTS The exposure of colorectal cancer cells with BCI resulted in antitumor effects associated with cell cycle arrest and induction of apoptosis. BCI exhibited strong cytotoxicity on DLD1, HT-29, and Caco-2 cells. BCI showed no significant interaction with irinotecan, but strongly attenuated the anticancer activity of oxaliplatin when administered together. Analysis of synergy potential further confirmed the antagonistic interaction between these two compounds. In silico investigation indicated CDK5 as a potential new target of BCI. CONCLUSIONS Our studies point to the anticancer potential of BCI but note the need for a precise mechanism of action.
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Affiliation(s)
- Beata Marciniak
- Department of Molecular Biotechnology and Genetics, University of Lodz, Banacha 12/16, 90-237 Lodz, Poland; (M.K.); (K.B.); (R.G.)
| | - Mateusz Kciuk
- Department of Molecular Biotechnology and Genetics, University of Lodz, Banacha 12/16, 90-237 Lodz, Poland; (M.K.); (K.B.); (R.G.)
- Doctoral School of Exact and Natural Sciences, University of Lodz, 90-237 Lodz, Poland
| | - Somdutt Mujwar
- Chitkara College of Pharmacy, Chitkara University, Rajpura 140401, Punjab, India;
| | - Rajamanikandan Sundaraj
- Centre for Drug Discovery, Department of Biochemistry, Karpagam Academy of Higher Education, Coimbatore 641021, Tamil Nadu, India;
| | - Karol Bukowski
- Department of Molecular Biotechnology and Genetics, University of Lodz, Banacha 12/16, 90-237 Lodz, Poland; (M.K.); (K.B.); (R.G.)
| | - Renata Gruszka
- Department of Molecular Biotechnology and Genetics, University of Lodz, Banacha 12/16, 90-237 Lodz, Poland; (M.K.); (K.B.); (R.G.)
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Boscolo Galazzo I, Tonin L, Miladinović A, Storti SF. Editorial: Brain-connectivity-based computer interfaces. Front Hum Neurosci 2023; 17:1281446. [PMID: 37736145 PMCID: PMC10509283 DOI: 10.3389/fnhum.2023.1281446] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Accepted: 08/28/2023] [Indexed: 09/23/2023] Open
Affiliation(s)
| | - Luca Tonin
- Department of Information Engineering, University of Padua, Padua, Italy
- Padova Neuroscience Center, University of Padua, Padua, Italy
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Aurucci GV, Preatoni G, Damiani A, Raspopovic S. Brain-Computer Interface to Deliver Individualized Multisensory Intervention for Neuropathic Pain. Neurotherapeutics 2023; 20:1316-1329. [PMID: 37407726 PMCID: PMC10480109 DOI: 10.1007/s13311-023-01396-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/25/2023] [Indexed: 07/07/2023] Open
Abstract
To unravel the complexity of the neuropathic pain experience, researchers have tried to identify reliable pain signatures (biomarkers) using electroencephalography (EEG) and skin conductance (SC). Nevertheless, their use as a clinical aid to design personalized therapies remains scarce and patients are prescribed with common and inefficient painkillers. To address this need, novel non-pharmacological interventions, such as transcutaneous electrical nerve stimulation (TENS) to activate peripheral pain relief via neuromodulation and virtual reality (VR) to modulate patients' attention, have emerged. However, all present treatments suffer from the inherent bias of the patient's self-reported pain intensity, depending on their predisposition and tolerance, together with unspecific, pre-defined scheduling of sessions which does not consider the timing of pain episodes onset. Here, we show a Brain-Computer Interface (BCI) detecting in real-time neurophysiological signatures of neuropathic pain from EEG combined with SC and accordingly triggering a multisensory intervention combining TENS and VR. After validating that the multisensory intervention effectively decreased experimentally induced pain, the BCI was tested with thirteen healthy subjects by electrically inducing pain and showed 82% recall in decoding pain in real time. Such constructed BCI was then validated with eight neuropathic patients reaching 75% online pain precision, and consequently releasing the intervention inducing a significant decrease (50% NPSI score) in neuropathic patients' pain perception. Our results demonstrate the feasibility of real-time pain detection from objective neurophysiological signals, and the effectiveness of a triggered combination of VR and TENS to decrease neuropathic pain. This paves the way towards personalized, data-driven pain therapies using fully portable technologies.
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Affiliation(s)
- Giuseppe Valerio Aurucci
- Laboratory for Neuroengineering, Department of Health Science and Technology, Institute for Robotics and Intelligent Systems, ETH Zürich, 8092, Zurich, Switzerland
| | - Greta Preatoni
- Laboratory for Neuroengineering, Department of Health Science and Technology, Institute for Robotics and Intelligent Systems, ETH Zürich, 8092, Zurich, Switzerland
| | - Arianna Damiani
- Laboratory for Neuroengineering, Department of Health Science and Technology, Institute for Robotics and Intelligent Systems, ETH Zürich, 8092, Zurich, Switzerland
| | - Stanisa Raspopovic
- Laboratory for Neuroengineering, Department of Health Science and Technology, Institute for Robotics and Intelligent Systems, ETH Zürich, 8092, Zurich, Switzerland.
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Awais MA, Redmond P, Ward TE, Healy G. AMBER: advancing multimodal brain-computer interfaces for enhanced robustness-A dataset for naturalistic settings. Front Neurogenom 2023; 4:1216440. [PMID: 38234491 PMCID: PMC10790919 DOI: 10.3389/fnrgo.2023.1216440] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Accepted: 08/01/2023] [Indexed: 01/19/2024]
Affiliation(s)
- Muhammad Ahsan Awais
- Insight Science Foundation Ireland Research Centre for Data Analytics, School of Computing, Dublin City University, Dublin, Ireland
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Vatrano M, Nemirovsky IE, Tonin P, Riganello F. Assessing Consciousness through Neurofeedback and Neuromodulation: Possibilities and Challenges. Life (Basel) 2023; 13:1675. [PMID: 37629532 PMCID: PMC10455583 DOI: 10.3390/life13081675] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Revised: 07/27/2023] [Accepted: 07/30/2023] [Indexed: 08/27/2023] Open
Abstract
Neurofeedback is a non-invasive therapeutic approach that has gained traction in recent years, showing promising results for various neurological and psychiatric conditions. It involves real-time monitoring of brain activity, allowing individuals to gain control over their own brainwaves and improve cognitive performance or alleviate symptoms. The use of electroencephalography (EEG), such as brain-computer interface (BCI), transcranial direct current stimulation (tDCS), and transcranial magnetic stimulation (TMS), has been instrumental in developing neurofeedback techniques. However, the application of these tools in patients with disorders of consciousness (DoC) presents unique challenges. In this narrative review, we explore the use of neurofeedback in treating patients with DoC. More specifically, we discuss the advantages and challenges of using tools such as EEG neurofeedback, tDCS, TMS, and BCI for these conditions. Ultimately, we hope to provide the neuroscientific community with a comprehensive overview of neurofeedback and emphasize its potential therapeutic applications in severe cases of impaired consciousness levels.
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Affiliation(s)
- Martina Vatrano
- S. Anna Institute, Research in Advanced Neurorehabilitation, Via Siris, 11, 88900 Crotone, Italy;
| | - Idan Efim Nemirovsky
- Department of Physics and Astronomy, Brain and Mind Institute, University of Western Ontario, London, ON N6A 3K7, Canada;
| | - Paolo Tonin
- S. Anna Institute, Research in Advanced Neurorehabilitation, Via Siris, 11, 88900 Crotone, Italy;
| | - Francesco Riganello
- S. Anna Institute, Research in Advanced Neurorehabilitation, Via Siris, 11, 88900 Crotone, Italy;
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Rouzitalab A, Boulay CB, Park J, Sachs AJ. Intracortical brain-computer interfaces in primates: a review and outlook. Biomed Eng Lett 2023; 13:375-390. [PMID: 37519868 PMCID: PMC10382423 DOI: 10.1007/s13534-023-00286-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Revised: 05/04/2023] [Accepted: 05/14/2023] [Indexed: 08/01/2023] Open
Abstract
Brain-computer interfaces (BCI) translate brain signals into artificial output to restore or replace natural central nervous system (CNS) functions. Multiple processes, including sensorimotor integration, decision-making, motor planning, execution, and updating, are involved in any movement. For example, a BCI may be better able to restore naturalistic motor behaviors if it uses signals from multiple brain areas and decodes natural behaviors' cognitive and motor aspects. This review provides an overview of the preliminary information necessary to plan a BCI project focusing on intracortical implants in primates. Since the brain structure and areas of non-human primates (NHP) are similar to humans, exploring the result of NHP studies will eventually benefit human BCI studies. The different types of BCI systems based on the target cortical area, types of signals, and decoding methods will be discussed. In addition, various successful state-of-the-art cases will be reviewed in more detail, focusing on the general algorithm followed in the real-time system. Finally, an outlook for improving the current BCI research studies will be debated.
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Affiliation(s)
- Alireza Rouzitalab
- School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON K1N 6N5 Canada
- The Ottawa Hospital Research Institute, Ottawa, ON Canada
| | | | - Jeongwon Park
- School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON K1N 6N5 Canada
- Department of Electrical and Biomedical Engineering, University of Nevada, Reno, NV 89557 USA
| | - Adam J. Sachs
- The Ottawa Hospital Research Institute, Ottawa, ON Canada
- The University of Ottawa Brain and Mind Research Institute, Ottawa, ON Canada
- Division of Neurosurgery, Department of Surgery, The Ottawa Hospital, Ottawa, ON Canada
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Dong Y, Wen X, Gao F, Gao C, Cao R, Xiang J, Cao R. Subject-Independent EEG Classification of Motor Imagery Based on Dual-Branch Feature Fusion. Brain Sci 2023; 13:1109. [PMID: 37509039 PMCID: PMC10377689 DOI: 10.3390/brainsci13071109] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2023] [Revised: 07/13/2023] [Accepted: 07/19/2023] [Indexed: 07/30/2023] Open
Abstract
A brain computer interface (BCI) system helps people with motor dysfunction interact with the external environment. With the advancement of technology, BCI systems have been applied in practice, but their practicability and usability are still greatly challenged. A large amount of calibration time is often required before BCI systems are used, which can consume the patient's energy and easily lead to anxiety. This paper proposes a novel motion-assisted method based on a novel dual-branch multiscale auto encoder network (MSAENet) to decode human brain motion imagery intentions, while introducing a central loss function to compensate for the shortcomings of traditional classifiers that only consider inter-class differences and ignore intra-class coupling. The effectiveness of the method is validated on three datasets, namely BCIIV2a, SMR-BCI and OpenBMI, to achieve zero calibration of the MI-BCI system. The results show that our proposed network displays good results on all three datasets. In the case of subject-independence, the MSAENet outperformed the other four comparison methods on the BCIIV2a and SMR-BCI datasets, while achieving F1_score values as high as 69.34% on the OpenBMI dataset. Our method maintains better classification accuracy with a small number of parameters and short prediction times, and the method achieves zero calibration of the MI-BCI system.
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Affiliation(s)
- Yanqing Dong
- School of Software, Taiyuan University of Technology, Taiyuan 030024, China
| | - Xin Wen
- School of Software, Taiyuan University of Technology, Taiyuan 030024, China
| | - Fang Gao
- School of Software, Taiyuan University of Technology, Taiyuan 030024, China
| | - Chengxin Gao
- School of Software, Taiyuan University of Technology, Taiyuan 030024, China
| | - Ruochen Cao
- School of Software, Taiyuan University of Technology, Taiyuan 030024, China
| | - Jie Xiang
- College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan 030024, China
| | - Rui Cao
- School of Software, Taiyuan University of Technology, Taiyuan 030024, China
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Noble SC, Woods E, Ward T, Ringwood JV. Adaptive P300-Based Brain-Computer Interface for Attention Training: Protocol for a Randomized Controlled Trial. JMIR Res Protoc 2023; 12:e46135. [PMID: 37405822 DOI: 10.2196/46135] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 05/10/2023] [Accepted: 05/23/2023] [Indexed: 07/06/2023] Open
Abstract
BACKGROUND The number of people with cognitive deficits and diseases, such as stroke, dementia, or attention-deficit/hyperactivity disorder, is rising due to an aging, or in the case of attention-deficit/hyperactivity disorder, a growing population. Neurofeedback training using brain-computer interfaces is emerging as a means of easy-to-use and noninvasive cognitive training and rehabilitation. A novel application of neurofeedback training using a P300-based brain-computer interface has previously shown potential to improve attention in healthy adults. OBJECTIVE This study aims to accelerate attention training using iterative learning control to optimize the task difficulty in an adaptive P300 speller task. Furthermore, we hope to replicate the results of a previous study using a P300 speller for attention training, as a benchmark comparison. In addition, the effectiveness of personalizing the task difficulty during training will be compared to a nonpersonalized task difficulty adaptation. METHODS In this single-blind, parallel, 3-arm randomized controlled trial, 45 healthy adults will be recruited and randomly assigned to the experimental group or 1 of 2 control groups. This study involves a single training session, where participants receive neurofeedback training through a P300 speller task. During this training, the task's difficulty is progressively increased, which makes it more difficult for the participants to maintain their performance. This encourages the participants to improve their focus. Task difficulty is either adapted based on the participants' performance (in the experimental group and control group 1) or chosen randomly (in control group 2). Changes in brain patterns before and after training will be analyzed to study the effectiveness of the different approaches. Participants will complete a random dot motion task before and after the training so that any transfer effects of the training to other cognitive tasks can be evaluated. Questionnaires will be used to estimate the participants' fatigue and compare the perceived workload of the training between groups. RESULTS This study has been approved by the Maynooth University Ethics Committee (BSRESC-2022-2474456) and is registered on ClinicalTrials.gov (NCT05576649). Participant recruitment and data collection began in October 2022, and we expect to publish the results in 2023. CONCLUSIONS This study aims to accelerate attention training using iterative learning control in an adaptive P300 speller task, making it a more attractive training option for individuals with cognitive deficits due to its ease of use and speed. The successful replication of the results from the previous study, which used a P300 speller for attention training, would provide further evidence to support the effectiveness of this training tool. TRIAL REGISTRATION ClinicalTrials.gov NCT05576649; https://clinicaltrials.gov/ct2/show/NCT05576649. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) DERR1-10.2196/46135.
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Affiliation(s)
- Sandra-Carina Noble
- Department of Electronic Engineering, Maynooth University, Maynooth, Ireland
| | - Eva Woods
- Department of Biology, Maynooth University, Maynooth, Ireland
| | - Tomas Ward
- Insight Science Foundation Ireland Research Centre for Data Analytics, Dublin City University, Dublin, Ireland
| | - John V Ringwood
- Department of Electronic Engineering, Maynooth University, Maynooth, Ireland
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Li R, Zhang Y, Fan G, Li Z, Li J, Fan S, Lou C, Liu X. Design and implementation of high sampling rate and multichannel wireless recorder for EEG monitoring and SSVEP response detection. Front Neurosci 2023; 17:1193950. [PMID: 37457014 PMCID: PMC10339741 DOI: 10.3389/fnins.2023.1193950] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Accepted: 05/30/2023] [Indexed: 07/18/2023] Open
Abstract
Introduction The collection and process of human brain activity signals play an essential role in developing brain-computer interface (BCI) systems. A portable electroencephalogram (EEG) device has become an important tool for monitoring brain activity and diagnosing mental diseases. However, the miniaturization, portability, and scalability of EEG recorder are the current bottleneck in the research and application of BCI. Methods For scalp EEG and other applications, the current study designs a 32-channel EEG recorder with a sampling rate up to 30 kHz and 16-bit accuracy, which can meet both the demands of scalp and intracranial EEG signal recording. A fully integrated electrophysiology microchip RHS2116 controlled by FPGA is employed to build the EEG recorder, and the design meets the requirements of high sampling rate, high transmission rate and channel extensive. Results The experimental results show that the developed EEG recorder provides a maximum 30 kHz sampling rate and 58 Mbps wireless transmission rate. The electrophysiological experiments were performed on scalp and intracranial EEG collection. An inflatable helmet with adjustable contact impedance was designed, and the pressurization can improve the SNR by approximately 4 times, the average accuracy of steady-state visual evoked potential (SSVEP) was 93.12%. Animal experiments were also performed on rats, and spike activity was captured successfully. Conclusion The designed multichannel wireless EEG collection system is simple and comfort, the helmet-EEG recorder can capture the bioelectric signals without noticeable interference, and it has high measurement performance and great potential for practical application in BCI systems.
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Affiliation(s)
- Ruikai Li
- The College of Electronic Information Engineering and the Hebei Key Laboratory of Digital Medical Engineering, Hebei University, Baoding, China
- Information Center, The Affiliated Hospital of Hebei University, Baoding, China
| | - Yixing Zhang
- The College of Electronic Information Engineering and the Hebei Key Laboratory of Digital Medical Engineering, Hebei University, Baoding, China
| | - Guangwei Fan
- The College of Electronic Information Engineering and the Hebei Key Laboratory of Digital Medical Engineering, Hebei University, Baoding, China
| | - Ziteng Li
- The College of Electronic Information Engineering and the Hebei Key Laboratory of Digital Medical Engineering, Hebei University, Baoding, China
| | - Jun Li
- The College of Electronic Information Engineering and the Hebei Key Laboratory of Digital Medical Engineering, Hebei University, Baoding, China
| | - Shiyong Fan
- The College of Electronic Information Engineering and the Hebei Key Laboratory of Digital Medical Engineering, Hebei University, Baoding, China
| | - Cunguang Lou
- The College of Electronic Information Engineering and the Hebei Key Laboratory of Digital Medical Engineering, Hebei University, Baoding, China
| | - Xiuling Liu
- The College of Electronic Information Engineering and the Hebei Key Laboratory of Digital Medical Engineering, Hebei University, Baoding, China
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47
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Kopecek M, Kremlacek J. Eye-tracking control of an adjustable electric bed: construction and validation by immobile patients with multiple sclerosis. J Neuroeng Rehabil 2023; 20:75. [PMID: 37296480 PMCID: PMC10251586 DOI: 10.1186/s12984-023-01193-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Accepted: 05/10/2023] [Indexed: 06/12/2023] Open
Abstract
BACKGROUND In severe conditions of limited motor abilities, frequent position changes for work or passive and active rest are essential bedside activities to prevent further health complications. We aimed to develop a system using eye movements for bed positioning and to verify its functionality in a control group and a group of patients with significant motor limitation caused by multiple sclerosis. METHODS The eye-tracking system utilized an innovative digital-to-analog converter module to control the positioning bed via a novel graphical user interface. We verified the ergonomics and usability of the system by performing a fixed sequence of positioning tasks, in which the leg and head support was repeatedly raised and then lowered. Fifteen women and eleven men aged 42.7 ± 15.9 years in the control group and nine women and eight men aged 60.3 ± 9.14 years in the patient group participated in the experiment. The degree of disability, according to the Expanded Disability Status Scale (EDSS), ranged from 7 to 9.5 points in the patients. We assessed the speed and efficiency of the bed control and the improvement during testing. In a questionnaire, we evaluated satisfaction with the system. RESULTS The control group mastered the task in 40.2 s (median) with an interquartile interval from 34.5 to 45.5 s, and patients mastered the task in in 56.5 (median) with an interquartile interval from 46.5 to 64.9 s. The efficiency of solving the task (100% corresponds to an optimal performance) was 86.3 (81.6; 91.0) % for the control group and 72.1 (63.0; 75.2) % for the patient group. Throughout testing, the patients learned to communicate with the system, and their efficiency and task time improved. A correlation analysis showed a negative relationship (rho = - 0.587) between efficiency improvement and the degree of impairment (EDSS). In the control group, the learning was not significant. On the questionnaire survey, sixteen patients reported gaining confidence in bed control. Seven patients preferred the offered form of bed control, and in six cases, they would choose another form of interface. CONCLUSIONS The proposed system and communication through eye movements are reliable for positioning the bed in people affected by advanced multiple sclerosis. Seven of 17 patients indicated that they would choose this system for bed control and wished to extend it for another application.
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Affiliation(s)
- Martin Kopecek
- Department of Medical Biophysics, Faculty of Medicine in Hradec Kralove, Charles University, Simkova 870, Hradec Kralove, Czech Republic
| | - Jan Kremlacek
- Department of Medical Biophysics, Faculty of Medicine in Hradec Kralove, Charles University, Simkova 870, Hradec Kralove, Czech Republic
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48
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Almajidy RK, Mottaghi S, Ajwad AA, Boudria Y, Mankodiya K, Besio W, Hofmann UG. A case for hybrid BCIs: combining optical and electrical modalities improves accuracy. Front Hum Neurosci 2023; 17:1162712. [PMID: 37351363 PMCID: PMC10282188 DOI: 10.3389/fnhum.2023.1162712] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Accepted: 05/18/2023] [Indexed: 06/24/2023] Open
Abstract
Near-infrared spectroscopy (NIRS) is a promising research tool that found its way into the field of brain-computer interfacing (BCI). BCI is crucially dependent on maximized usability thus demanding lightweight, compact, and low-cost hardware. We designed, built, and validated a hybrid BCI system incorporating one optical and two electrical modalities ameliorating usability issues. The novel hardware consisted of a NIRS device integrated with an electroencephalography (EEG) system that used two different types of electrodes: Regular gelled gold disk electrodes and tri-polar concentric ring electrodes (TCRE). BCI experiments with 16 volunteers implemented a two-dimensional motor imagery paradigm in off- and online sessions. Various non-canonical signal processing methods were used to extract and classify useful features from EEG, tEEG (EEG through TCRE electrodes), and NIRS. Our analysis demonstrated evidence of improvement in classification accuracy when using the TCRE electrodes compared to disk electrodes and the NIRS system. Based on our synchronous hybrid recording system, we could show that the combination of NIRS-EEG-tEEG performed significantly better than either single modality only.
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Affiliation(s)
- Rand Kasim Almajidy
- Faculty of Medicine, University of Freiburg, Freiburg im Breisgau, Germany
- Section for Neuroelectronic Systems, Department of Neurosurgery, Medical Center University of Freiburg, Freiburg im Breisgau, Germany
| | - Soheil Mottaghi
- Roche Diagnostics Automation Solutions GmbH, Ludwigsburg, Germany
| | - Asmaa A. Ajwad
- College of Medicine, University of Diyala, Baqubah, Iraq
| | - Yacine Boudria
- Electro Standards Laboratories, Cranston, RI, United States
| | - Kunal Mankodiya
- Electrical, Computer and Biomedical Engineering, Kingston, RI, United States
| | - Walter Besio
- Electrical, Computer and Biomedical Engineering, Kingston, RI, United States
| | - Ulrich G. Hofmann
- Faculty of Medicine, University of Freiburg, Freiburg im Breisgau, Germany
- Section for Neuroelectronic Systems, Department of Neurosurgery, Medical Center University of Freiburg, Freiburg im Breisgau, Germany
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Tang X, Zhang W, Wang H, Wang T, Tan C, Zou M, Xu Z. Dynamic pruning group equivariant network for motor imagery EEG recognition. Front Bioeng Biotechnol 2023; 11:917328. [PMID: 37324415 PMCID: PMC10267707 DOI: 10.3389/fbioe.2023.917328] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Accepted: 04/26/2023] [Indexed: 06/17/2023] Open
Abstract
Introduction: The decoding of the motor imaging electroencephalogram (MI-EEG) is the most critical part of the brain-computer interface (BCI) system. However, the inherent complexity of EEG signals makes it challenging to analyze and model them. Methods: In order to effectively extract and classify the features of EEG signals, a classification algorithm of motor imagery EEG signals based on dynamic pruning equal-variant group convolutional network is proposed. Group convolutional networks can learn powerful representations based on symmetric patterns, but they lack clear methods to learn meaningful relationships between them. The dynamic pruning equivariant group convolution proposed in this paper is used to enhance meaningful symmetric combinations and suppress unreasonable and misleading symmetric combinations. At the same time, a new dynamic pruning method is proposed to dynamically evaluate the importance of parameters, which can restore the pruned connections. Results and Discussion: The experimental results show that the pruning group equivariant convolution network is superior to the traditional benchmark method in the benchmark motor imagery EEG data set. This research can also be transferred to other research areas.
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Affiliation(s)
- Xianlun Tang
- Department of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Wei Zhang
- Department of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Huiming Wang
- Department of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Tianzhu Wang
- Department of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Cong Tan
- Department of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Mi Zou
- Department of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Zihui Xu
- Xinqiao Hospital, Army Medical University, Chongqing, China
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50
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Batistić L, Sušanj D, Pinčić D, Ljubic S. Motor Imagery Classification Based on EEG Sensing with Visual and Vibrotactile Guidance. Sensors (Basel) 2023; 23:s23115064. [PMID: 37299791 DOI: 10.3390/s23115064] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 05/15/2023] [Accepted: 05/23/2023] [Indexed: 06/12/2023]
Abstract
Motor imagery (MI) is a technique of imagining the performance of a motor task without actually using the muscles. When employed in a brain-computer interface (BCI) supported by electroencephalographic (EEG) sensors, it can be used as a successful method of human-computer interaction. In this paper, the performance of six different classifiers, namely linear discriminant analysis (LDA), support vector machine (SVM), random forest (RF), and three classifiers from the family of convolutional neural networks (CNN), is evaluated using EEG MI datasets. The study investigates the effectiveness of these classifiers on MI, guided by a static visual cue, dynamic visual guidance, and a combination of dynamic visual and vibrotactile (somatosensory) guidance. The effect of filtering passband during data preprocessing was also investigated. The results show that the ResNet-based CNN significantly outperforms the competing classifiers on both vibrotactile and visually guided data when detecting different directions of MI. Preprocessing the data using low-frequency signal features proves to be a better solution to achieve higher classification accuracy. It has also been shown that vibrotactile guidance has a significant impact on classification accuracy, with the associated improvement particularly evident for architecturally simpler classifiers. These findings have important implications for the development of EEG-based BCIs, as they provide valuable insight into the suitability of different classifiers for different contexts of use.
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Affiliation(s)
- Luka Batistić
- University of Rijeka, Faculty of Engineering, Vukovarska 58, HR-51000 Rijeka, Croatia
- Center for Artificial Intelligence and Cybersecurity, University of Rijeka, R. Matejcic 2, HR-51000 Rijeka, Croatia
| | - Diego Sušanj
- University of Rijeka, Faculty of Engineering, Vukovarska 58, HR-51000 Rijeka, Croatia
| | - Domagoj Pinčić
- University of Rijeka, Faculty of Engineering, Vukovarska 58, HR-51000 Rijeka, Croatia
| | - Sandi Ljubic
- University of Rijeka, Faculty of Engineering, Vukovarska 58, HR-51000 Rijeka, Croatia
- Center for Artificial Intelligence and Cybersecurity, University of Rijeka, R. Matejcic 2, HR-51000 Rijeka, Croatia
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