Cooperative Identification of Prolonged Motor Movement From EEG for BCI Without Feedback
This paper presents a novel approach for recognition of prolonged motor movements from a subject’s electroencephalogram (EEG) using orthogonal functions to model a sequence of sub-gestures. In this approach, an individual’s EEG signals corresponding to physical (or imagery) con...
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| Format: | Article |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10838554/ |
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| author | Alicia Falcon-Caro Joao Filipe Ferreira Saeid Sanei |
| author_facet | Alicia Falcon-Caro Joao Filipe Ferreira Saeid Sanei |
| author_sort | Alicia Falcon-Caro |
| collection | DOAJ |
| description | This paper presents a novel approach for recognition of prolonged motor movements from a subject’s electroencephalogram (EEG) using orthogonal functions to model a sequence of sub-gestures. In this approach, an individual’s EEG signals corresponding to physical (or imagery) continuous movement for different gestures are divided into segments associated with their related sub-gestures. Then, a diffusion adaptation approach is introduced to model the interface between the brain neural activity and the corresponding gesture dynamics. In such a formulation, orthogonal Bessel functions are utilized to represent different gestures and used as the target for the adaptation algorithm. This method aims at detecting and evaluating the prolonged motor movements as well as identifying highly complex sub-gestures. This technique can perform satisfactory classification even in the presence of small data sizes while, unlike many regressors, maintaining a low computational cost. The method has been validated using two different publicly available EEG datasets. An inter-subject average validation accuracy of 70% after performing a leave-one-subject-out k-fold cross-validation is obtained for the classification of the smallest dataset when ten sub-gestures are considered. |
| format | Article |
| id | doaj-art-849028ad9fa24bcaa6bc708d310c9c23 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-849028ad9fa24bcaa6bc708d310c9c232025-01-24T00:01:49ZengIEEEIEEE Access2169-35362025-01-0113117651177710.1109/ACCESS.2025.352853910838554Cooperative Identification of Prolonged Motor Movement From EEG for BCI Without FeedbackAlicia Falcon-Caro0https://orcid.org/0000-0002-1085-7716Joao Filipe Ferreira1Saeid Sanei2https://orcid.org/0000-0002-1446-5744Department of Computer Science, Nottingham Trent University, Nottingham, U.K.Department of Computer Science, Nottingham Trent University, Nottingham, U.K.Department of Computer Science, Nottingham Trent University, Nottingham, U.K.This paper presents a novel approach for recognition of prolonged motor movements from a subject’s electroencephalogram (EEG) using orthogonal functions to model a sequence of sub-gestures. In this approach, an individual’s EEG signals corresponding to physical (or imagery) continuous movement for different gestures are divided into segments associated with their related sub-gestures. Then, a diffusion adaptation approach is introduced to model the interface between the brain neural activity and the corresponding gesture dynamics. In such a formulation, orthogonal Bessel functions are utilized to represent different gestures and used as the target for the adaptation algorithm. This method aims at detecting and evaluating the prolonged motor movements as well as identifying highly complex sub-gestures. This technique can perform satisfactory classification even in the presence of small data sizes while, unlike many regressors, maintaining a low computational cost. The method has been validated using two different publicly available EEG datasets. An inter-subject average validation accuracy of 70% after performing a leave-one-subject-out k-fold cross-validation is obtained for the classification of the smallest dataset when ten sub-gestures are considered.https://ieeexplore.ieee.org/document/10838554/Bessel functionsbrain-computer interfacecooperative networksEEGprolonged movement |
| spellingShingle | Alicia Falcon-Caro Joao Filipe Ferreira Saeid Sanei Cooperative Identification of Prolonged Motor Movement From EEG for BCI Without Feedback IEEE Access Bessel functions brain-computer interface cooperative networks EEG prolonged movement |
| title | Cooperative Identification of Prolonged Motor Movement From EEG for BCI Without Feedback |
| title_full | Cooperative Identification of Prolonged Motor Movement From EEG for BCI Without Feedback |
| title_fullStr | Cooperative Identification of Prolonged Motor Movement From EEG for BCI Without Feedback |
| title_full_unstemmed | Cooperative Identification of Prolonged Motor Movement From EEG for BCI Without Feedback |
| title_short | Cooperative Identification of Prolonged Motor Movement From EEG for BCI Without Feedback |
| title_sort | cooperative identification of prolonged motor movement from eeg for bci without feedback |
| topic | Bessel functions brain-computer interface cooperative networks EEG prolonged movement |
| url | https://ieeexplore.ieee.org/document/10838554/ |
| work_keys_str_mv | AT aliciafalconcaro cooperativeidentificationofprolongedmotormovementfromeegforbciwithoutfeedback AT joaofilipeferreira cooperativeidentificationofprolongedmotormovementfromeegforbciwithoutfeedback AT saeidsanei cooperativeidentificationofprolongedmotormovementfromeegforbciwithoutfeedback |