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Zhang K, Hu X. Unsupervised separation of nonlinearly mixed event-related potentials using manifold clustering and non-negative matrix factorization. Comput Biol Med 2024; 178:108700. [PMID: 38852400 DOI: 10.1016/j.compbiomed.2024.108700] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Revised: 05/12/2024] [Accepted: 06/01/2024] [Indexed: 06/11/2024]
Abstract
Event-related potentials (ERPs) can quantify brain responses to reveal the neural mechanisms of sensory perception. However, ERPs often reflect nonlinear mixture responses to multiple sources of sensory stimuli, and an accurate separation of the response to each stimulus remains a challenge. This study aimed to separate the ERP into nonlinearly mixed source components specific to individual stimuli. We developed an unsupervised learning method based on clustering of manifold structures of mixture signals combined with channel optimization for signal source reconstruction using non-negative matrix factorization (NMF). Specifically, we first implemented manifold learning based on Local Tangent Space Alignment (LTSA) to extract the spatial manifold structure of multi-resolution sub-signals separated via wavelet packet transform. We then used fuzzy entropy to extract the dynamical process of the manifold structures and performed a k-means clustering to separate different sources. Lastly, we used NMF to obtain the optimal contributions of multiple channels to ensure accurate source reconstructions. We evaluated our developed approach using a simulated ERP dataset with known ground truth of two components of ERP mixture signals. Our results show that the correlation coefficient between the reconstructed source signal and the true source signal was 92.8 % and that the separation accuracy in ERP amplitude was 91.6 %. The results show that our unsupervised separation approach can accurately separate ERP signals from nonlinear mixture source components. The outcomes provide a promising way to isolate brain responses to multiple stimulus sources during multisensory perception.
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Affiliation(s)
- Kai Zhang
- Department of Mechanical Engineering, Pennsylvania State University, University Park, USA
| | - Xiaogang Hu
- Department of Mechanical Engineering, Pennsylvania State University, University Park, USA; Department of Kinesiology, Pennsylvania State University, University Park, USA; Department of Physical Medicine & Rehabilitation, Pennsylvania State Hershey College of Medicine, USA; Huck Institutes of the Life Sciences, Pennsylvania State University, University Park, USA; Center for Neural Engineering, Pennsylvania State University, University Park, USA.
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2
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Blanco-Díaz CF, Guerrero-Mendez CD, Delisle-Rodriguez D, Jaramillo-Isaza S, Ruiz-Olaya AF, Frizera-Neto A, Ferreira de Souza A, Bastos-Filho T. Evaluation of temporal, spatial and spectral filtering in CSP-based methods for decoding pedaling-based motor tasks using EEG signals. Biomed Phys Eng Express 2024; 10:035003. [PMID: 38417162 DOI: 10.1088/2057-1976/ad2e35] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Accepted: 02/28/2024] [Indexed: 03/01/2024]
Abstract
Stroke is a neurological syndrome that usually causes a loss of voluntary control of lower/upper body movements, making it difficult for affected individuals to perform Activities of Daily Living (ADLs). Brain-Computer Interfaces (BCIs) combined with robotic systems, such as Motorized Mini Exercise Bikes (MMEB), have enabled the rehabilitation of people with disabilities by decoding their actions and executing a motor task. However, Electroencephalography (EEG)-based BCIs are affected by the presence of physiological and non-physiological artifacts. Thus, movement discrimination using EEG become challenging, even in pedaling tasks, which have not been well explored in the literature. In this study, Common Spatial Patterns (CSP)-based methods were proposed to classify pedaling motor tasks. To address this, Filter Bank Common Spatial Patterns (FBCSP) and Filter Bank Common Spatial-Spectral Patterns (FBCSSP) were implemented with different spatial filtering configurations by varying the time segment with different filter bank combinations for the three methods to decode pedaling tasks. An in-house EEG dataset during pedaling tasks was registered for 8 participants. As results, the best configuration corresponds to a filter bank with two filters (8-19 Hz and 19-30 Hz) using a time window between 1.5 and 2.5 s after the cue and implementing two spatial filters, which provide accuracy of approximately 0.81, False Positive Rates lower than 0.19, andKappaindex of 0.61. This work implies that EEG oscillatory patterns during pedaling can be accurately classified using machine learning. Therefore, our method can be applied in the rehabilitation context, such as MMEB-based BCIs, in the future.
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Affiliation(s)
- Cristian Felipe Blanco-Díaz
- Postgraduate Program in Electrical Engineering, Federal University of Espirito Santo (UFES), 29075-910 Vitória, Brazil
- Faculty of Mechanical, Electronics and Biomedical Engineering, Antonio Nariño University, Bogotá D.C, Colombia
| | - Cristian David Guerrero-Mendez
- Postgraduate Program in Electrical Engineering, Federal University of Espirito Santo (UFES), 29075-910 Vitória, Brazil
- Faculty of Mechanical, Electronics and Biomedical Engineering, Antonio Nariño University, Bogotá D.C, Colombia
| | | | | | - Andrés Felipe Ruiz-Olaya
- Faculty of Mechanical, Electronics and Biomedical Engineering, Antonio Nariño University, Bogotá D.C, Colombia
| | - Anselmo Frizera-Neto
- Postgraduate Program in Electrical Engineering, Federal University of Espirito Santo (UFES), 29075-910 Vitória, Brazil
| | | | - Teodiano Bastos-Filho
- Postgraduate Program in Electrical Engineering, Federal University of Espirito Santo (UFES), 29075-910 Vitória, Brazil
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EEG Channel Selection Techniques in Motor Imagery Applications: A Review and New Perspectives. BIOENGINEERING (BASEL, SWITZERLAND) 2022; 9:bioengineering9120726. [PMID: 36550932 PMCID: PMC9774545 DOI: 10.3390/bioengineering9120726] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Revised: 10/28/2022] [Accepted: 10/30/2022] [Indexed: 11/25/2022]
Abstract
Communication, neuro-prosthetics, and environmental control are just a few applications for disabled persons who use robots and manipulators that use brain-computer interface (BCI) systems. The brain's motor imagery (MI) signal is an essential input for a brain-related task in BCI applications. Due to their noninvasive, portability, and cost-effectiveness, electroencephalography (EEG) signals are the most widely used input in BCI systems. The EEG data are often collected from more than 100 different locations in the brain; channel selection techniques are critical for selecting the optimum channels for a given application. However, when analyzing EEG data, the principal purpose of channel selection is to reduce computational complexity, improve classification accuracy by avoiding overfitting, and reduce setup time. Several channel selection assessment algorithms, both with and without classification-based methods, extracted appropriate channel subsets using defined criteria. Therefore, based on the exhaustive analysis of the EEG channel selection, this manuscript analyses several existing studies to reduce the number of noisy channels and improve system performance. We review several existing works to find the most promising MI-based EEG channel selection algorithms and associated classification methodologies on various datasets. Moreover, we focus on channel selection methods that choose fewer channels with great precision. Finally, our main finding is that a smaller channel set, typically 10-30% of total channels, provided excellent performance compared to other existing studies.
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Triana-Guzman N, Orjuela-Cañon AD, Jutinico AL, Mendoza-Montoya O, Antelis JM. Decoding EEG rhythms offline and online during motor imagery for standing and sitting based on a brain-computer interface. Front Neuroinform 2022; 16:961089. [PMID: 36120085 PMCID: PMC9481272 DOI: 10.3389/fninf.2022.961089] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2022] [Accepted: 08/03/2022] [Indexed: 12/02/2022] Open
Abstract
Motor imagery (MI)-based brain-computer interface (BCI) systems have shown promising advances for lower limb motor rehabilitation. The purpose of this study was to develop an MI-based BCI for the actions of standing and sitting. Thirty-two healthy subjects participated in the study using 17 active EEG electrodes. We used a combination of the filter bank common spatial pattern (FBCSP) method and the regularized linear discriminant analysis (RLDA) technique for decoding EEG rhythms offline and online during motor imagery for standing and sitting. The offline analysis indicated the classification of motor imagery and idle state provided a mean accuracy of 88.51 ± 1.43% and 85.29 ± 1.83% for the sit-to-stand and stand-to-sit transitions, respectively. The mean accuracies of the sit-to-stand and stand-to-sit online experiments were 94.69 ± 1.29% and 96.56 ± 0.83%, respectively. From these results, we believe that the MI-based BCI may be useful to future brain-controlled standing systems.
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Affiliation(s)
| | | | - Andres L. Jutinico
- Facultad de Ingeniería Mecánica, Electrónica y Biomédica, Universidad Antonio Nariño, Bogota, Colombia
| | - Omar Mendoza-Montoya
- Tecnologico de Monterrey, Escuela de Ingeniería y Ciencias, Monterrey, Mexico
- *Correspondence: Omar Mendoza-Montoya
| | - Javier M. Antelis
- Tecnologico de Monterrey, Escuela de Ingeniería y Ciencias, Monterrey, Mexico
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Dillen A, Lathouwers E, Miladinović A, Marusic U, Ghaffari F, Romain O, Meeusen R, De Pauw K. A data-driven machine learning approach for brain-computer interfaces targeting lower limb neuroprosthetics. Front Hum Neurosci 2022; 16:949224. [PMID: 35966996 PMCID: PMC9364873 DOI: 10.3389/fnhum.2022.949224] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Accepted: 06/30/2022] [Indexed: 11/13/2022] Open
Abstract
Prosthetic devices that replace a lost limb have become increasingly performant in recent years. Recent advances in both software and hardware allow for the decoding of electroencephalogram (EEG) signals to improve the control of active prostheses with brain-computer interfaces (BCI). Most BCI research is focused on the upper body. Although BCI research for the lower extremities has increased in recent years, there are still gaps in our knowledge of the neural patterns associated with lower limb movement. Therefore, the main objective of this study is to show the feasibility of decoding lower limb movements from EEG data recordings. The second aim is to investigate whether well-known neuroplastic adaptations in individuals with an amputation have an influence on decoding performance. To address this, we collected data from multiple individuals with lower limb amputation and a matched able-bodied control group. Using these data, we trained and evaluated common BCI methods that have already been proven effective for upper limb BCI. With an average test decoding accuracy of 84% for both groups, our results show that it is possible to discriminate different lower extremity movements using EEG data with good accuracy. There are no significant differences (p = 0.99) in the decoding performance of these movements between healthy subjects and subjects with lower extremity amputation. These results show the feasibility of using BCI for lower limb prosthesis control and indicate that decoding performance is not influenced by neuroplasticity-induced differences between the two groups.
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Affiliation(s)
- Arnau Dillen
- Human Physiology and Sports Physiotherapy Research Group, Vrije Universiteit Brussel, Brussels, Belgium
- Brussels Human Robotics Research Center, Vrije Universiteit Brussel, Brussels, Belgium
- Équipes Traitement de l'Information et Systèmes, CY Cergy Paris University, Cergy, France
| | - Elke Lathouwers
- Human Physiology and Sports Physiotherapy Research Group, Vrije Universiteit Brussel, Brussels, Belgium
- Brussels Human Robotics Research Center, Vrije Universiteit Brussel, Brussels, Belgium
| | - Aleksandar Miladinović
- Institute for Kinesiology Research, Science and Research Centre Koper, Koper, Slovenia
- Institute for Maternal and Child Health - IRCCS Burlo Garofolo, Trieste, Italy
- Department Engineering and Architecture, University of Trieste, Trieste, Italy
| | - Uros Marusic
- Institute for Kinesiology Research, Science and Research Centre Koper, Koper, Slovenia
- Department of Health Sciences, Alma Mater Europaea - ECM, Maribor, Slovenia
| | - Fakhreddine Ghaffari
- Équipes Traitement de l'Information et Systèmes, CY Cergy Paris University, Cergy, France
| | - Olivier Romain
- Équipes Traitement de l'Information et Systèmes, CY Cergy Paris University, Cergy, France
| | - Romain Meeusen
- Human Physiology and Sports Physiotherapy Research Group, Vrije Universiteit Brussel, Brussels, Belgium
- Brussels Human Robotics Research Center, Vrije Universiteit Brussel, Brussels, Belgium
| | - Kevin De Pauw
- Human Physiology and Sports Physiotherapy Research Group, Vrije Universiteit Brussel, Brussels, Belgium
- Brussels Human Robotics Research Center, Vrije Universiteit Brussel, Brussels, Belgium
- *Correspondence: Kevin De Pauw
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Channel selection from source localization: A review of four EEG-based brain-computer interfaces paradigms. Behav Res Methods 2022:10.3758/s13428-022-01897-2. [PMID: 35794417 DOI: 10.3758/s13428-022-01897-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/27/2022] [Indexed: 11/08/2022]
Abstract
Channel selection is a critical part of the classification procedure for multichannel electroencephalogram (EEG)-based brain-computer interfaces (BCI). An optimized subset of electrodes reduces computational complexity and optimizes accuracy. Different tasks activate different sources in the brain and are characterized by distinctive channels. The goal of the current review is to define a subset of electrodes for each of four popular BCI paradigms: motor imagery, motor execution, steady-state visual evoked potentials and P300. Twenty-one studies have been reviewed to identify the most significant activations of cortical sources. The relevant EEG sensors are determined from the reported 3D Talairach coordinates. They are scored by their weighted mean Cohen's d and its confidence interval, providing the magnitude of the corresponding effect size and its statistical significance. Our goal is to create a knowledge-based channel selection framework with a sufficient statistical power. The core channel selection (CCS) could be used as a reference by EEG researchers and would have the advantages of practicality and rapidity, allowing for an easy implementation of semiparametric algorithms.
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Xu L, Chavez-Echeagaray ME, Berisha V. Unsupervised EEG channel selection based on nonnegative matrix factorization. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103700] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Asanza V, Peláez E, Loayza F, Lorente-Leyva LL, Peluffo-Ordóñez DH. Identification of Lower-Limb Motor Tasks via Brain-Computer Interfaces: A Topical Overview. SENSORS (BASEL, SWITZERLAND) 2022; 22:2028. [PMID: 35271175 PMCID: PMC8914806 DOI: 10.3390/s22052028] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Revised: 02/11/2022] [Accepted: 02/23/2022] [Indexed: 02/01/2023]
Abstract
Recent engineering and neuroscience applications have led to the development of brain-computer interface (BCI) systems that improve the quality of life of people with motor disabilities. In the same area, a significant number of studies have been conducted in identifying or classifying upper-limb movement intentions. On the contrary, few works have been concerned with movement intention identification for lower limbs. Notwithstanding, lower-limb neurorehabilitation is a major topic in medical settings, as some people suffer from mobility problems in their lower limbs, such as those diagnosed with neurodegenerative disorders, such as multiple sclerosis, and people with hemiplegia or quadriplegia. Particularly, the conventional pattern recognition (PR) systems are one of the most suitable computational tools for electroencephalography (EEG) signal analysis as the explicit knowledge of the features involved in the PR process itself is crucial for both improving signal classification performance and providing more interpretability. In this regard, there is a real need for outline and comparative studies gathering benchmark and state-of-art PR techniques that allow for a deeper understanding thereof and a proper selection of a specific technique. This study conducted a topical overview of specialized papers covering lower-limb motor task identification through PR-based BCI/EEG signal analysis systems. To do so, we first established search terms and inclusion and exclusion criteria to find the most relevant papers on the subject. As a result, we identified the 22 most relevant papers. Next, we reviewed their experimental methodologies for recording EEG signals during the execution of lower limb tasks. In addition, we review the algorithms used in the preprocessing, feature extraction, and classification stages. Finally, we compared all the algorithms and determined which of them are the most suitable in terms of accuracy.
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Affiliation(s)
- Víctor Asanza
- Facultad de Ingeniería en Electricidad y Computación, Escuela Superior Politécnica del Litoral (ESPOL), Campus Gustavo Galindo km 30.5 Vía Perimetral, Guayaquil P.O. Box 09-01-5863, Ecuador;
| | - Enrique Peláez
- Facultad de Ingeniería en Electricidad y Computación, Escuela Superior Politécnica del Litoral (ESPOL), Campus Gustavo Galindo km 30.5 Vía Perimetral, Guayaquil P.O. Box 09-01-5863, Ecuador;
| | - Francis Loayza
- Neuroimaging and Bioengineering Laboratory (LNB), Facultad de Ingeniería en Mecánica y Ciencias de la Producción, Escuela Superior Politécnica del Litoral (ESPOL), Campus Gustavo Galindo km 30.5 Vía Perimetral, Guayaquil P.O. Box 09-01-5863, Ecuador;
| | | | - Diego H. Peluffo-Ordóñez
- Faculty of Engineering, Corporación Universitaria Autónoma de Nariño, Pasto 520001, Colombia;
- Modeling, Simulation and Data Analysis (MSDA) Research Program, Mohammed VI Polytechnic University, Ben Guerir 43150, Morocco
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10
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Stress Classification by Multimodal Physiological Signals Using Variational Mode Decomposition and Machine Learning. JOURNAL OF HEALTHCARE ENGINEERING 2021; 2021:2146369. [PMID: 34484651 PMCID: PMC8413068 DOI: 10.1155/2021/2146369] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/06/2021] [Revised: 07/22/2021] [Accepted: 08/12/2021] [Indexed: 12/02/2022]
Abstract
In this pandemic situation, importance and awareness about mental health are getting more attention. Stress recognition from multimodal sensor based physiological signals such as electroencephalogram (EEG) and electrocardiography (ECG) signals is a very cost-effective way due to its noninvasive nature. A dataset, recorded during the mental arithmetic task, consisting of EEG + ECG signals of 36 participants is used. It contains two categories of performance, namely, “Good” (nonstressed) and “Bad” (stressed) (Gupta et al. 2018 and Eraldeír et al. 2018). This paper presents an effective approach for the recognition of stress marker at frontal, temporal, central, and occipital lobes. It processes the multimodality physiological signals. The variational mode decomposition (VMD) strategy is used for data preprocessing and for the decomposition of signals into various oscillatory mode functions. Poincare plots (PP) are derived from the first eight variational modes and features from these plots have been extracted such as mean, area, and central tendency measure of the elliptical region. The statistical significance of the extracted features with p < 0.5 has been performed using the Wilcoxson test. The multilayer perceptron (MPLN) and Support Vector Machine (SVM) algorithms are used for the classification of stress and nonstress categories. MLPN has achieved the maximum accuracies of 100% for frontal and temporal lobes. The suggested method can be incorporated in noninvasive EEG signal processing based automated stress identification systems.
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Aliakbaryhosseinabadi S, Dosen S, Savic AM, Blicher J, Farina D, Mrachacz-Kersting N. Participant-specific classifier tuning increases the performance of hand movement detection from EEG in patients with amyotrophic lateral sclerosis. J Neural Eng 2021; 18. [PMID: 34280899 DOI: 10.1088/1741-2552/ac15e3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Accepted: 07/19/2021] [Indexed: 11/11/2022]
Abstract
Objective.Brain-computer interface (BCI) systems can be employed to provide motor and communication assistance to patients suffering from neuromuscular diseases, such as amyotrophic lateral sclerosis (ALS). Movement related cortical potentials (MRCPs), which are naturally generated during movement execution, can be used to implement a BCI triggered by motor attempts. Such BCI could assist impaired motor functions of ALS patients during disease progression, and facilitate the training for the generation of reliable MRCPs. The training aspect is relevant to establish a communication channel in the late stage of the disease. Therefore, the aim of this study was to investigate the possibility of detecting MRCPs associated to movement intention in ALS patients with different levels of disease progression from slight to complete paralysis.Approach.Electroencephalography signals were recorded from nine channels in 30 ALS patients at various stages of the disease while they performed or attempted to perform hand movements timed to a visual cue. The movement detection was implemented using offline classification between movement and rest phase. Temporal and spectral features were extracted using 500 ms sliding windows with 50% overlap. The detection was tested for each individual channel and two surrogate channels by performing feature selection followed by classification using linear and non-linear support vector machine and linear discriminant analysis.Main results.The results demonstrated that the detection performance was high in all patients (accuracy 80.5 ± 5.6%) but that the classification parameters (channel, features and classifier) leading to the best performance varied greatly across patients. When the same channel and classifier were used for all patients (participant-generic analysis), the performance significantly decreased (accuracy 74 ± 8.3%).Significance.The present study demonstrates that to maximize the detection of brain waves across ALS patients at different stages of the disease, the classification pipeline should be tuned to each patient individually.
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Affiliation(s)
| | - Strahinja Dosen
- Department of Health Science and Technology, Aalborg University, Aalborg, Denmark
| | - Andrej M Savic
- Science and Research Centre, University of Belgrade-School of Electrical Engineering, Belgrade 11000, Serbia
| | - Jakob Blicher
- Department of Clinical Medicine, Center of Functionally Integrative Neuroscience, Århus University, Aarhus, Denmark
| | - Dario Farina
- Department of Bioengineering, Imperial College London, London, United Kingdom
| | - Natalie Mrachacz-Kersting
- Department of Sport and Sport Science, Albert-Ludwigs University Freiburg, Freiburg im Breisgau, Germany
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Xu L, Xu M, Jung TP, Ming D. Review of brain encoding and decoding mechanisms for EEG-based brain-computer interface. Cogn Neurodyn 2021; 15:569-584. [PMID: 34367361 PMCID: PMC8286913 DOI: 10.1007/s11571-021-09676-z] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Revised: 03/10/2021] [Accepted: 03/26/2021] [Indexed: 01/04/2023] Open
Abstract
A brain-computer interface (BCI) can connect humans and machines directly and has achieved successful applications in the past few decades. Many new BCI paradigms and algorithms have been developed in recent years. Therefore, it is necessary to review new progress in BCIs. This paper summarizes progress for EEG-based BCIs from the perspective of encoding paradigms and decoding algorithms, which are two key elements of BCI systems. Encoding paradigms are grouped by their underlying neural meachanisms, namely sensory- and motor-related, vision-related, cognition-related and hybrid paradigms. Decoding algorithms are reviewed in four categories, namely decomposition algorithms, Riemannian geometry, deep learning and transfer learning. This review will provide a comprehensive overview of both modern primary paradigms and algorithms, making it helpful for those who are developing BCI systems.
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Affiliation(s)
- Lichao Xu
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
| | - Minpeng Xu
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
- Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China
| | - Tzyy-Ping Jung
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
- Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China
- Swartz Center for Computational Neuroscience, University of California, San Diego, USA
| | - Dong Ming
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
- Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China
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Gu Y, Li X, Chen S, Li X. AOAR: an automatic ocular artifact removal approach for multi-channel electroencephalogram data based on non-negative matrix factorization and empirical mode decomposition. J Neural Eng 2021; 18:056012. [PMID: 33821810 DOI: 10.1088/1741-2552/abede0] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
OBJECTIVE Electroencephalogram (EEG) signals suffer inevitable interference from artifacts during the acquisition process. These artifacts make the analysis and interpretation of EEG data difficult. A major source of artifacts in EEGs is ocular activity. Therefore, it is important to remove ocular artifacts before further processing the EEG data. APPROACH In this study, an automatic ocular artifact removal (AOAR) method for EEG signals is proposed based on non-negative matrix factorization (NMF) and empirical mode decomposition (EMD). First, the amplitude of EEG data was normalized in order to ensure its non-negativity. Then, the normalized EEG data were decomposed into a set of components using NMF. The components containing ocular artifacts were extracted automatically through the fractal dimension. Subsequently, the temporal activities of these components were adaptively decomposed into some intrinsic mode functions (IMFs) by EMD. The IMFs corresponding to ocular artifacts were removed. Finally, the de-noised EEG data were reconstructed. MAIN RESULTS The proposed method was tested against seven other methods. In order to assess the effectiveness and reliability of the AOAR method in processing EEG data, experiments on ocular artifact removal were performed using simulated EEG data. Experimental results indicated that the proposed method was superior to the other methods in terms of root mean square error, signal-to-noise ratio (SNR) and correlation coefficient, especially in cases with a lower SNR. To further evaluate the potential applications of the proposed method in real life, the proposed method and others were applied to preprocess real EEG data recorded from children with and without attention-deficit/hyperactivity disorder (ADHD). After artifact rejection, the event-related potential feature was extracted for classification. The AOAR method was best at distinguishing the children with ADHD from the others. SIGNIFICANCE These results indicate that the proposed AOAR method has excellent prospects for removing ocular artifacts from EEG data.
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Affiliation(s)
- Yue Gu
- Key Laboratory of Computer Vision and System (Ministry of Education), School of Computer Science and Engineering, Tianjin University of Technology, Tianjin 300384, People's Republic of China. Engineering Research Center of Learning-Based Intelligent system, Ministry of Education, Tianjin 300384, People's Republic of China
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Zuo C, Jin J, Xu R, Wu L, Liu C, Miao Y, Wang X. Cluster decomposing and multi-objective optimization based-ensemble learning framework for motor imagery-based brain-computer interfaces. J Neural Eng 2021; 18. [PMID: 33524961 DOI: 10.1088/1741-2552/abe20f] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2020] [Accepted: 02/01/2021] [Indexed: 11/12/2022]
Abstract
OBJECTIVE Motor imagery (MI) is a mental representation of motor behavior and a widely used pattern in electroencephalogram (EEG) based brain-computer interface (BCI) systems. EEG is known for its non-stationary, non-linear features and sensitivity to artifacts from various sources. This study aimed to design a powerful classifier with a strong generalization capability for MI based BCIs. APPROACH In this study, we proposed a cluster decomposing based ensemble learning framework (CDECL) for EEG classification of MI based BCIs. The EEG data was decomposed into sub-data sets with different distributions by clustering decomposition. Then a set of heterogeneous classifiers was trained on each sub-data set for generating a diversified classifier search space. To obtain the optimal classifier combination, the ensemble learning was formulated as a multi-objective optimization problem and a stochastic fractal based binary multi-objective fruit fly optimization algorithm was proposed for solving the ensemble learning problem. MAIN RESULTS The proposed method was validated on two public EEG datasets (BCI Competition IV datasets IIb and BCI Competition IV dataset IIa) and compared with several other competing classification methods. Experimental results showed that the proposed CDECL based methods can effectively construct a diversity ensemble classifier and exhibits superior classification performance in comparison with several competing methods. SIGNIFICANCE The proposed method is promising for improving the performance of MI-based BCIs.
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Affiliation(s)
- Cili Zuo
- East China University of Science and Technology, 130 Meilong road, Shanghai, Shanghai, 200237, CHINA
| | - Jing Jin
- Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education, East China University of Science and Technology, 130 Meilong Road, Xuhui District, Shanghai 200237, SHANGHAI, 200237, CHINA
| | - Ren Xu
- Guger Technologies OG, Sierningstrasse 14, Graz, 8020, AUSTRIA
| | - Lianghong Wu
- Hunan University of Science and Technology, Tiaoyuan Road, Xiangtan, 411201, CHINA
| | - Chang Liu
- East China University of Science and Technology, 130 Meilong Road, Shanghai, Shanghai, 200237, CHINA
| | - Yangyang Miao
- East China University of Science and Technology, 130 Meilong raod, Shanghai, Shanghai, 200237, CHINA
| | - Xingyu Wang
- East China University of Science and Technology, 130 Meilong Road, Shanghai, Shanghai, 200237, CHINA
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15
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Browarska N, Kawala-Sterniuk A, Zygarlicki J, Podpora M, Pelc M, Martinek R, Gorzelańczyk EJ. Comparison of Smoothing Filters' Influence on Quality of Data Recorded with the Emotiv EPOC Flex Brain-Computer Interface Headset during Audio Stimulation. Brain Sci 2021; 11:98. [PMID: 33451080 PMCID: PMC7828570 DOI: 10.3390/brainsci11010098] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Revised: 01/02/2021] [Accepted: 01/08/2021] [Indexed: 12/15/2022] Open
Abstract
Off-the-shelf, consumer-grade EEG equipment is nowadays becoming the first-choice equipment for many scientists when it comes to recording brain waves for research purposes. On one hand, this is perfectly understandable due to its availability and relatively low cost (especially in comparison to some clinical-level EEG devices), but, on the other hand, quality of the recorded signals is gradually increasing and reaching levels that were offered just a few years ago by much more expensive devices used in medicine for diagnostic purposes. In many cases, a well-designed filter and/or a well-thought signal acquisition method improve the signal quality to the level that it becomes good enough to become subject of further analysis allowing to formulate some valid scientific theories and draw far-fetched conclusions related to human brain operation. In this paper, we propose a smoothing filter based upon the Savitzky-Golay filter for the purpose of EEG signal filtering. Additionally, we provide a summary and comparison of the applied filter to some other approaches to EEG data filtering. All the analyzed signals were acquired from subjects performing visually involving high-concentration tasks with audio stimuli using Emotiv EPOC Flex equipment.
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Affiliation(s)
- Natalia Browarska
- Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, 45-758 Opole, Poland; (N.B.); (J.Z.); (M.P.); (M.P.)
| | - Aleksandra Kawala-Sterniuk
- Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, 45-758 Opole, Poland; (N.B.); (J.Z.); (M.P.); (M.P.)
| | - Jaroslaw Zygarlicki
- Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, 45-758 Opole, Poland; (N.B.); (J.Z.); (M.P.); (M.P.)
| | - Michal Podpora
- Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, 45-758 Opole, Poland; (N.B.); (J.Z.); (M.P.); (M.P.)
| | - Mariusz Pelc
- Faculty of Electrical Engineering, Automatic Control and Informatics, Opole University of Technology, 45-758 Opole, Poland; (N.B.); (J.Z.); (M.P.); (M.P.)
- Department of Computing and Information Systems, University of Greenwich, London SE10 9LS, UK
| | - Radek Martinek
- Department of Cybernetics and Biomedical Engineering, FEECS, VSB-Technical University Ostrava, 708 00 Ostrava-Poruba, Czech Republic;
| | - Edward Jacek Gorzelańczyk
- Department of Theoretical Basis of BioMedical Sciences and Medical Informatics, Nicolaus Copernicus University, Collegium Medicum, 85-067 Bydgoszcz, Poland;
- Institute of Philosophy, Kazimierz Wielki University, 85-092 Bydgoszcz, Poland
- Outpatient Addiction Treatment, Babinski Specialist Psychiatric Healthcare Center, 91-229 Lodz, Poland
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16
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Liu X, Lv L, Shen Y, Xiong P, Yang J, Liu J. Multiscale space-time-frequency feature-guided multitask learning CNN for motor imagery EEG classification. J Neural Eng 2021; 18. [PMID: 33395676 DOI: 10.1088/1741-2552/abd82b] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Accepted: 01/04/2021] [Indexed: 11/11/2022]
Abstract
OBJECTIVE Motor imagery (MI) electroencephalography (EEG) classification is regarded as a promising technology for brain--computer interface (BCI) systems, which help people to communicate with the outside world using neural activities. However, decoding human intent accurately is a challenging task because of its small signal-to-noise ratio and non-stationary characteristics. Methods that directly extract features from raw EEG signals ignores key frequency domain information. One of the challenges in MI classification tasks is finding a way to supplement the frequency domain information ignored by the raw EEG signal. APPROACH In this study, we fuse different models using their complementary characteristics to develop a multiscale space-time-frequency feature-guided multitask learning convolutional neural network (CNN) architecture. The proposed method consists of four modules: the space-time feature-based representation module, time-frequency feature-based representation module, multimodal fused feature-guided generation module, and classification module. The proposed framework is based on multitask learning. The four modules are trained using three tasks simultaneously and jointly optimised. RESULTS The proposed method is evaluated using three public challenge datasets. Through quantitative analysis, we demonstrate that our proposed method outperforms most state-of-the-art machine learning and deep learning techniques for EEG classification, thereby demonstrating the robustness and effectiveness of our method. Moreover, the proposed method is employed to realize control of robot based on EEG signal, verifying its feasibility in real-time applications. SIGNIFICANCE To the best of our knowledge, a deep CNN architecture that fuses different input cases, which have complementary characteristics, has not been applied to BCI tasks. Because of the interaction of the three tasks in the multitask learning architecture, our method can improve the generalisation and accuracy of subject-dependent and subject-independent methods with limited annotated data.
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Affiliation(s)
- Xiuling Liu
- Hebei University, No. 180 Wusi Dong Road, Lian Chi District, Baoding City, Hebei Province, China, Baoding, Hebei, 071002, CHINA
| | - Linyang Lv
- Hebei University, No. 180 Wusi Dong Road, Lian Chi District, Baoding City, Hebei Province, China, Baoding, Hebei, 071002, CHINA
| | - Yonglong Shen
- Hebei University, No. 180 Wusi Dong Road, Lian Chi District, Baoding City, Hebei Province, China, Baoding, Hebei, 071002, CHINA
| | - Peng Xiong
- Hebei University, No. 180 Wusi Dong Road, Lian Chi District, Baoding City, Hebei Province, China, Baoding, 071002, CHINA
| | - Jianli Yang
- Hebei University, No. 180 Wusi Dong Road, Lian Chi District, Baoding City, Hebei Province, China, Baoding, Hebei, 071002, CHINA
| | - Jing Liu
- Hebei Normal University, No.20 Road East. 2nd Ring South, Shijiazhuang, 050024, CHINA
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17
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Prieur-Coloma Y, Delisle-Rodriguez D, Mayeta-Revilla L, Gurve D, Reinoso-Leblanch RA, Lopez-Delis A, Bastos T, Krishnan S, da Rocha AF. Shoulder Flexion Pre-Movement Recognition Through Subject-Specific Brain Regions to Command an Upper Limb Exoskeleton. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:3848-3851. [PMID: 33018840 DOI: 10.1109/embc44109.2020.9175263] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
This work presents two brain-computer interfaces (BCIs) for shoulder pre-movement recognition using: 1) manual strategy for Electroencephalography (EEG) channels selection, and 2) subject-specific channels selection by applying non-negative factorization matrix (NMF). Besides, the proposed BCIs compute spatial features extracted from filtered EEG signals through Riemannian covariance matrices and a linear discriminant analysis (LDA) to discriminate both shoulder pre-movement and rest states. We studied on twenty-one healthy subjects different frequency ranges looking the best frequency band for shoulder pre-movement recognition. As a result, our BCI located automatically EEG channels on the contralateral moved limb, and enhancing the pre-movement recognition (ACC = 71.39 ± 12.68%, κ = 0.43 ± 0.25%). The ability of the proposed BCIs to select specific EEG locations more cortically related to the moved limb could benefit the neuro-rehabilitation process.
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18
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Hu G, Zhou T, Luo S, Mahini R, Xu J, Chang Y, Cong F. Assessment of nonnegative matrix factorization algorithms for electroencephalography spectral analysis. Biomed Eng Online 2020; 19:61. [PMID: 32736630 PMCID: PMC7393858 DOI: 10.1186/s12938-020-00796-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2020] [Accepted: 06/09/2020] [Indexed: 02/01/2023] Open
Abstract
BACKGROUND Nonnegative matrix factorization (NMF) has been successfully used for electroencephalography (EEG) spectral analysis. Since NMF was proposed in the 1990s, many adaptive algorithms have been developed. However, the performance of their use in EEG data analysis has not been fully compared. Here, we provide a comparison of four NMF algorithms in terms of accuracy of estimation, stability (repeatability of the results) and time complexity of algorithms with simulated data. In the practical application of NMF algorithms, stability plays an important role, which was an emphasis in the comparison. A Hierarchical clustering algorithm was implemented to evaluate the stability of NMF algorithms. RESULTS In simulation-based comprehensive analysis of fit, stability, accuracy of estimation and time complexity, hierarchical alternating least squares (HALS) low-rank NMF algorithm (lraNMF_HALS) outperformed the other three NMF algorithms. In the application of lraNMF_HALS for real resting-state EEG data analysis, stable and interpretable features were extracted. CONCLUSION Based on the results of assessment, our recommendation is to use lraNMF_HALS, providing the most accurate and robust estimation.
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Affiliation(s)
- Guoqiang Hu
- School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, China
| | - Tianyi Zhou
- School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, China.,Center for Cognition and Neuroergonomics, State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University at Zhuhai, Zhuhai, China
| | - Siwen Luo
- Affiliated Zhongshan Hospital of Dalian University, Dalian, China
| | - Reza Mahini
- School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, China
| | - Jing Xu
- Department of Neurology and Psychiatry, First Affiliated Hospital, Dalian Medical University, Dalian, China.
| | - Yi Chang
- Department of Neurology and Psychiatry, First Affiliated Hospital, Dalian Medical University, Dalian, China.
| | - Fengyu Cong
- School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, China. .,School of Artificial Intelligence, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, China. .,Key Laboratory of Integrated Circuit and Biomedical Electronic System, Dalian University of Technology, Dalian, Liaoning, China. .,Faculty of Information Technology, University of Jyvaskyla, Jyvaskyla, Finland.
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19
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Gurve D, Delisle-Rodriguez D, Bastos-Filho T, Krishnan S. Trends in Compressive Sensing for EEG Signal Processing Applications. SENSORS (BASEL, SWITZERLAND) 2020; 20:E3703. [PMID: 32630685 PMCID: PMC7374282 DOI: 10.3390/s20133703] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/25/2020] [Revised: 06/17/2020] [Accepted: 06/23/2020] [Indexed: 11/16/2022]
Abstract
The tremendous progress of big data acquisition and processing in the field of neural engineering has enabled a better understanding of the patient's brain disorders with their neural rehabilitation, restoration, detection, and diagnosis. An integration of compressive sensing (CS) and neural engineering emerges as a new research area, aiming to deal with a large volume of neurological data for fast speed, long-term, and energy-saving purposes. Furthermore, electroencephalography (EEG) signals for brain-computer interfaces (BCIs) have shown to be very promising, with diverse neuroscience applications. In this review, we focused on EEG-based approaches which have benefited from CS in achieving fast and energy-saving solutions. In particular, we examine the current practices, scientific opportunities, and challenges of CS in the growing field of BCIs. We emphasized on summarizing major CS reconstruction algorithms, the sparse basis, and the measurement matrix used in CS to process the EEG signal. This literature review suggests that the selection of a suitable reconstruction algorithm, sparse basis, and measurement matrix can help to improve the performance of current CS-based EEG studies. In this paper, we also aim at providing an overview of the reconstruction free CS approach and the related literature in the field. Finally, we discuss the opportunities and challenges that arise from pushing the integration of the CS framework for BCI applications.
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Affiliation(s)
- Dharmendra Gurve
- Department of Electrical, Computer, and Biomedical Engineering, Ryerson University, Toronto, ON M5B 2K3, Canada;
| | - Denis Delisle-Rodriguez
- Postgraduate Program in Electrical Engineering, Federal University of Espirito Santo, Vitoria 29075-910, Brazil; (D.D.-R.); (T.B.-F.)
| | - Teodiano Bastos-Filho
- Postgraduate Program in Electrical Engineering, Federal University of Espirito Santo, Vitoria 29075-910, Brazil; (D.D.-R.); (T.B.-F.)
| | - Sridhar Krishnan
- Department of Electrical, Computer, and Biomedical Engineering, Ryerson University, Toronto, ON M5B 2K3, Canada;
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20
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Delgado Saa J, Christen A, Martin S, Pasley BN, Knight RT, Giraud AL. Using Coherence-based spectro-spatial filters for stimulus features prediction from electro-corticographic recordings. Sci Rep 2020; 10:7637. [PMID: 32376909 PMCID: PMC7203138 DOI: 10.1038/s41598-020-63303-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2019] [Accepted: 03/19/2020] [Indexed: 11/08/2022] Open
Abstract
The traditional approach in neuroscience relies on encoding models where brain responses are related to different stimuli in order to establish dependencies. In decoding tasks, on the contrary, brain responses are used to predict the stimuli, and traditionally, the signals are assumed stationary within trials, which is rarely the case for natural stimuli. We hypothesize that a decoding model assuming each experimental trial as a realization of a random process more likely reflects the statistical properties of the undergoing process compared to the assumption of stationarity. Here, we propose a Coherence-based spectro-spatial filter that allows for reconstructing stimulus features from brain signal's features. The proposed method extracts common patterns between features of the brain signals and the stimuli that produced them. These patterns, originating from different recording electrodes are combined, forming a spatial filter that produces a unified prediction of the presented stimulus. This approach takes into account frequency, phase, and spatial distribution of brain features, hence avoiding the need to predefine specific frequency bands of interest or phase relationships between stimulus and brain responses manually. Furthermore, the model does not require the tuning of hyper-parameters, reducing significantly the computational load attached to it. Using three different cognitive tasks (motor movements, speech perception, and speech production), we show that the proposed method consistently improves stimulus feature predictions in terms of correlation (group averages of 0.74 for motor movements, 0.84 for speech perception, and 0.74 for speech production) in comparison with other methods based on regularized multivariate regression, probabilistic graphical models and artificial neural networks. Furthermore, the model parameters revealed those anatomical regions and spectral components that were discriminant in the different cognitive tasks. This novel method does not only provide a useful tool to address fundamental neuroscience questions, but could also be applied to neuroprosthetics.
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Affiliation(s)
- Jaime Delgado Saa
- Auditory Language Group, University of Geneva, Geneva, Switzerland.
- BSPAI Lab, Universidad del Norte, Barranquilla, Colombia.
| | - Andy Christen
- Auditory Language Group, University of Geneva, Geneva, Switzerland
| | - Stephanie Martin
- Auditory Language Group, University of Geneva, Geneva, Switzerland
| | - Brian N Pasley
- Knight Lab, University of California at Berkeley, Berkeley, USA
| | - Robert T Knight
- Knight Lab, University of California at Berkeley, Berkeley, USA
| | - Anne-Lise Giraud
- Auditory Language Group, University of Geneva, Geneva, Switzerland
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