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Sengupta P, Lakshminarayanan K. Cortical activation and BCI performance during brief tactile imagery: A comparative study with motor imagery. Behav Brain Res 2024; 459:114760. [PMID: 37979923 DOI: 10.1016/j.bbr.2023.114760] [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: 08/30/2023] [Revised: 11/06/2023] [Accepted: 11/11/2023] [Indexed: 11/20/2023]
Abstract
Brain-computer interfaces (BCIs) rely heavily on motor imagery (MI) for operation, yet tactile imagery (TI) presents a novel approach that may be advantageous in situations where visual feedback is impractical. The current study aimed to compare the cortical activity and digit classification performance induced by TI and MI to assess the viability of TI for use in BCIs. Twelve right-handed participants engaged in trials of TI and MI, focusing on their left and right index digits. Event-related desynchronization (ERD) in the mu and beta bands was analyzed, and classification accuracy was determined through an artificial neural network (ANN). Comparable ERD patterns were observed in both TI and MI, with significant decreases in ERD during imagery tasks. The ANN demonstrated high classification accuracy, with TI achieving a mean±SD of 79.30 ± 3.91 % and MI achieving 81.10 ± 2.96 %, with no significant difference between the two (p = 0.11). The study found that TI induces substantial ERD comparable to MI and maintains high classification accuracy, supporting its potential as an effective mental strategy for BCIs. This suggests that TI could be a valuable alternative in BCI applications, particularly for individuals unable to rely on visual cues.
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Affiliation(s)
- Puja Sengupta
- Neuro-Rehabilitation Lab, Department of Sensors and Biomedical Technology, School of Electronics Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India
| | - Kishor Lakshminarayanan
- Neuro-Rehabilitation Lab, Department of Sensors and Biomedical Technology, School of Electronics Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India.
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Cao B, Niu H, Hao J, Yang X, Ye Z. Spatial Visual Imagery (SVI)-Based Electroencephalograph Discrimination for Natural CAD Manipulation. SENSORS (BASEL, SWITZERLAND) 2024; 24:785. [PMID: 38339501 PMCID: PMC10856899 DOI: 10.3390/s24030785] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Revised: 01/12/2024] [Accepted: 01/17/2024] [Indexed: 02/12/2024]
Abstract
With the increasing demand for natural interactions, people have realized that an intuitive Computer-Aided Design (CAD) interaction mode can reduce the complexity of CAD operation and improve the design experience. Although interaction modes like gaze and gesture are compatible with some complex CAD manipulations, they still require people to express their design intentions physically. The brain contains design intentions implicitly and controls the corresponding body parts that execute the task. Therefore, building an end-to-end channel between the brain and computer as an auxiliary mode for CAD manipulation will allow people to send design intentions mentally and make their interaction more intuitive. This work focuses on the 1-D translation scene and studies a spatial visual imagery (SVI) paradigm to provide theoretical support for building an electroencephalograph (EEG)-based brain-computer interface (BCI) for CAD manipulation. Based on the analysis of three spatial EEG features related to SVI (e.g., common spatial patterns, cross-correlation, and coherence), a multi-feature fusion-based discrimination model was built for SVI. The average accuracy of the intent discrimination of 10 subjects was 86%, and the highest accuracy was 93%. The method proposed was verified to be feasible for discriminating the intentions of CAD object translation with good classification performance. This work further proves the potential of BCI in natural CAD manipulation.
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Affiliation(s)
- Beining Cao
- School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China; (B.C.); (H.N.); (X.Y.); (Z.Y.)
| | - Hongwei Niu
- School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China; (B.C.); (H.N.); (X.Y.); (Z.Y.)
- Yangtze Delta Region Academy, Beijing Institute of Technology, Jiaxing 314019, China
- Key Laboratory of Industry Knowledge & Data Fusion Technology and Application, Ministry of Industry and Information Technology, Beijing Institute of Technology, Beijing 100081, China
| | - Jia Hao
- School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China; (B.C.); (H.N.); (X.Y.); (Z.Y.)
- Yangtze Delta Region Academy, Beijing Institute of Technology, Jiaxing 314019, China
- Key Laboratory of Industry Knowledge & Data Fusion Technology and Application, Ministry of Industry and Information Technology, Beijing Institute of Technology, Beijing 100081, China
| | - Xiaonan Yang
- School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China; (B.C.); (H.N.); (X.Y.); (Z.Y.)
- Yangtze Delta Region Academy, Beijing Institute of Technology, Jiaxing 314019, China
- Key Laboratory of Industry Knowledge & Data Fusion Technology and Application, Ministry of Industry and Information Technology, Beijing Institute of Technology, Beijing 100081, China
| | - Zinian Ye
- School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China; (B.C.); (H.N.); (X.Y.); (Z.Y.)
- Yangtze Delta Region Academy, Beijing Institute of Technology, Jiaxing 314019, China
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Fasipe G, Goršič M, Rahman MH, Rammer J. Community mobility and participation assessment of manual wheelchair users: a review of current techniques and challenges. Front Hum Neurosci 2024; 17:1331395. [PMID: 38249574 PMCID: PMC10796510 DOI: 10.3389/fnhum.2023.1331395] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Accepted: 12/11/2023] [Indexed: 01/23/2024] Open
Abstract
According to the World Health Organization, hundreds of individuals commence wheelchair use daily, often due to an injury such as spinal cord injury or through a condition such as a stroke. However, manual wheelchair users typically experience reductions in individual community mobility and participation. In this review, articles from 2017 to 2023 were reviewed to identify means of measuring community mobility and participation of manual wheelchair users, factors that can impact these aspects, and current rehabilitation techniques for improving them. The selected articles document current best practices utilizing self-surveys, in-clinic assessments, and remote tracking through GPS and accelerometer data, which rehabilitation specialists can apply to track their patients' community mobility and participation accurately. Furthermore, rehabilitation methods such as wheelchair training programs, brain-computer interface triggered functional electric stimulation therapy, and community-based rehabilitation programs show potential to improve the community mobility and participation of manual wheelchair users. Recommendations were made to highlight potential avenues for future research.
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Affiliation(s)
- Grace Fasipe
- Department of Biomedical Engineering, College of Engineering and Applied Science, University of Wisconsin-Milwaukee, Milwaukee, WI, United States
| | - Maja Goršič
- Department of Biomedical Engineering, College of Engineering and Applied Science, University of Wisconsin-Milwaukee, Milwaukee, WI, United States
| | - Mohammad Habibur Rahman
- Department of Biomedical Engineering, College of Engineering and Applied Science, University of Wisconsin-Milwaukee, Milwaukee, WI, United States
- Department of Mechanical Engineering, College of Engineering and Applied Science, University of Wisconsin-Milwaukee, Milwaukee, WI, United States
| | - Jacob Rammer
- Department of Biomedical Engineering, College of Engineering and Applied Science, University of Wisconsin-Milwaukee, Milwaukee, WI, United States
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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] [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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Kim SK, Kirchner EA. Detection of tactile-based error-related potentials (ErrPs) in human-robot interaction. Front Neurorobot 2023; 17:1297990. [PMID: 38162893 PMCID: PMC10756909 DOI: 10.3389/fnbot.2023.1297990] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Accepted: 11/22/2023] [Indexed: 01/03/2024] Open
Abstract
Robot learning based on implicitly extracted error detections (e.g., EEG-based error detections) has been well-investigated in human-robot interaction (HRI). In particular, the use of error-related potential (ErrP) evoked when recognizing errors is advantageous for robot learning when evaluation criteria cannot be explicitly defined, e.g., due to the complex behavior of robots. In most studies, erroneous behavior of robots were recognized visually. In some studies, visuo-tactile stimuli were used to evoke ErrPs or a tactile cue was used to indicate upcoming errors. To our knowledge, there are no studies in which ErrPs are evoked when recognizing errors only via the tactile channel. Hence, we investigated ErrPs evoked by tactile recognition of errors during HRI. In our scenario, subjects recognized errors caused by incorrect behavior of an orthosis during the execution of arm movements tactilely. EEG data from eight subjects was recorded. Subjects were asked to give a motor response to ensure error detection. Latency between the occurrence of errors and the response to errors was expected to be short. We assumed that the motor related brain activity is timely correlated with the ErrP and might be used from the classifier. To better interpret and test our results, we therefore tested ErrP detections in two additional scenarios, i.e., without motor response and with delayed motor response. In addition, we transferred three scenarios (motor response, no motor response, delayed motor response). Response times to error was short. However, high ErrP-classification performance was found for all subjects in case of motor response and no motor response condition. Further, ErrP classification performance was reduced for the transfer between motor response and delayed motor response, but not for the transfer between motor response and no motor response. We have shown that tactilely induced errors can be detected with high accuracy from brain activity. Our preliminary results suggest that also in tactile ErrPs the brain response is clear enough such that motor response is not relevant for classification. However, in future work, we will more systematically investigate tactile-based ErrP classification.
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Affiliation(s)
- Su Kyoung Kim
- Robotics Innovation Center, German Research Center for Artificial Intelligence GmbH, Bremen, Germany
| | - Elsa Andrea Kirchner
- Robotics Innovation Center, German Research Center for Artificial Intelligence GmbH, Bremen, Germany
- Institute of Medical Technology Systems, University of Duisburg-Essen, Duisburg, Germany
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Shkury E, Danziger-Schragenheim S, Katzir Z, Ezra Y, Giladi N, Mirelman A, Maidan I. Differences in EEG Event-Related Potentials during Dual Task in Parkinson's Disease Carriers and Non-Carriers of the G2019S-LRRK2 Mutation. SENSORS (BASEL, SWITZERLAND) 2023; 23:8266. [PMID: 37837096 PMCID: PMC10575245 DOI: 10.3390/s23198266] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Revised: 09/28/2023] [Accepted: 10/03/2023] [Indexed: 10/15/2023]
Abstract
BACKGROUND The G2019S-LRRK2 gene mutation is a common cause of hereditary Parkinson's disease (PD), associated with a higher frequency of the postural instability gait difficulty (PIGD) motor phenotype yet with preserved cognition. This study investigated neurophysiological changes during motor and cognitive tasks in PD patients with and without the G2019S-LRRK2 mutation. METHODS 33 iPD patients and 22 LRRK2-PD patients performed the visual Go/NoGo task (VGNG) during sitting (single-task) and walking (dual-task) while wearing a 64-channel EEG cap. Event-related potentials (ERP) from Fz and Pz, specifically N200 and P300, were extracted and analyzed to quantify brain activity patterns. RESULTS The LRRK2-PD group performed better in the VGNG than the iPD group (group*task; p = 0.05). During Go, the iPD group showed reduced N2 amplitude and prolonged N2 latency during walking, whereas the LRRK2-PD group showed only shorter latency (group*task p = 0.027). During NoGo, opposite patterns emerged; the iPD group showed reduced N2 and increased P3 amplitudes during walking while the LRRK2-PD group demonstrated increased N2 and reduced P3 (N2: group*task, p = 0.010, P3: group*task, p = 0.012). CONCLUSIONS The LRRK2-PD group showed efficient early cognitive processes, reflected by N2, resulting in greater neural synchronization and prominent ERPs. These processes are possibly the underlying mechanisms for the observed better cognitive performance as compared to the iPD group. As such, future applications of intelligent medical sensing should be capable of capturing these electrophysiological patterns in order to enhance motor-cognitive functions.
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Affiliation(s)
- Eden Shkury
- Laboratory of Early Markers of Neurodegeneration, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv 6423906, Israel; (E.S.); (S.D.-S.); (Z.K.); (Y.E.); (N.G.); (A.M.)
- School of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Shani Danziger-Schragenheim
- Laboratory of Early Markers of Neurodegeneration, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv 6423906, Israel; (E.S.); (S.D.-S.); (Z.K.); (Y.E.); (N.G.); (A.M.)
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Zoya Katzir
- Laboratory of Early Markers of Neurodegeneration, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv 6423906, Israel; (E.S.); (S.D.-S.); (Z.K.); (Y.E.); (N.G.); (A.M.)
- School of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Yael Ezra
- Laboratory of Early Markers of Neurodegeneration, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv 6423906, Israel; (E.S.); (S.D.-S.); (Z.K.); (Y.E.); (N.G.); (A.M.)
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Nir Giladi
- Laboratory of Early Markers of Neurodegeneration, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv 6423906, Israel; (E.S.); (S.D.-S.); (Z.K.); (Y.E.); (N.G.); (A.M.)
- School of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Anat Mirelman
- Laboratory of Early Markers of Neurodegeneration, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv 6423906, Israel; (E.S.); (S.D.-S.); (Z.K.); (Y.E.); (N.G.); (A.M.)
- School of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Inbal Maidan
- Laboratory of Early Markers of Neurodegeneration, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv 6423906, Israel; (E.S.); (S.D.-S.); (Z.K.); (Y.E.); (N.G.); (A.M.)
- School of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 6997801, Israel
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Cui Y, Xie S, Fu Y, Xie X. Predicting Motor Imagery BCI Performance Based on EEG Microstate Analysis. Brain Sci 2023; 13:1288. [PMID: 37759889 PMCID: PMC10526389 DOI: 10.3390/brainsci13091288] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Revised: 09/02/2023] [Accepted: 09/04/2023] [Indexed: 09/29/2023] Open
Abstract
Motor imagery (MI) electroencephalography (EEG) is natural and comfortable for controllers, and has become a research hotspot in the field of the brain-computer interface (BCI). Exploring the inter-subject MI-BCI performance variation is one of the fundamental problems in MI-BCI application. EEG microstates with high spatiotemporal resolution and multichannel information can represent brain cognitive function. In this paper, four EEG microstates (MS1, MS2, MS3, MS4) were used in the analysis of the differences in the subjects' MI-BCI performance, and the four microstate feature parameters (the mean duration, the occurrences per second, the time coverage ratio, and the transition probability) were calculated. The correlation between the resting-state EEG microstate feature parameters and the subjects' MI-BCI performance was measured. Based on the negative correlation of the occurrence of MS1 and the positive correlation of the mean duration of MS3, a resting-state microstate predictor was proposed. Twenty-eight subjects were recruited to participate in our MI experiments to assess the performance of our resting-state microstate predictor. The experimental results show that the average area under curve (AUC) value of our resting-state microstate predictor was 0.83, and increased by 17.9% compared with the spectral entropy predictor, representing that the microstate feature parameters can better fit the subjects' MI-BCI performance than spectral entropy predictor. Moreover, the AUC of microstate predictor is higher than that of spectral entropy predictor at both the single-session level and average level. Overall, our resting-state microstate predictor can help MI-BCI researchers better select subjects, save time, and promote MI-BCI development.
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Affiliation(s)
- Yujie Cui
- Shaanxi Joint International Research Center on Integrated Technique of Brain-Computer for Unmanned System, Northwestern Polytechnical University, Xi’an 710129, China; (Y.C.); (Y.F.); (X.X.)
| | - Songyun Xie
- Shaanxi Joint International Research Center on Integrated Technique of Brain-Computer for Unmanned System, Northwestern Polytechnical University, Xi’an 710129, China; (Y.C.); (Y.F.); (X.X.)
| | - Yingxin Fu
- Shaanxi Joint International Research Center on Integrated Technique of Brain-Computer for Unmanned System, Northwestern Polytechnical University, Xi’an 710129, China; (Y.C.); (Y.F.); (X.X.)
- Xi’an Aeronautics Computing Technique Research Institute, AVIC Xi’an, Xi’an 710068, China
| | - Xinzhou Xie
- Shaanxi Joint International Research Center on Integrated Technique of Brain-Computer for Unmanned System, Northwestern Polytechnical University, Xi’an 710129, China; (Y.C.); (Y.F.); (X.X.)
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