EEG Resolutions in Detecting and Decoding Finger Movements from Spectral Analysis
Mu/beta rhythms are well-studied brain activities that originate from sensorimotor cortices. These rhythms reveal spectral changes in alpha and beta bands induced by movements of different body parts, e.g. hands and limbs, in electroencephalography (EEG) signals. However, less can be revealed in the...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2015-09-01
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| Series: | Frontiers in Neuroscience |
| Subjects: | |
| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fnins.2015.00308/full |
| _version_ | 1818894431361695744 |
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| author | Ran eXiao Lei eDing |
| author_facet | Ran eXiao Lei eDing |
| author_sort | Ran eXiao |
| collection | DOAJ |
| description | Mu/beta rhythms are well-studied brain activities that originate from sensorimotor cortices. These rhythms reveal spectral changes in alpha and beta bands induced by movements of different body parts, e.g. hands and limbs, in electroencephalography (EEG) signals. However, less can be revealed in them about movements of different fine body parts that activate adjacent brain regions, such as individual fingers from one hand. Several studies have reported spatial and temporal couplings of rhythmic activities at different frequency bands, suggesting the existence of well-defined spectral structures across multiple frequency bands. In the present study, spectral principal component analysis (PCA) was applied on EEG data, obtained from a finger movement task, to identify cross-frequency spectral structures. Features from identified spectral structures were examined in their spatial patterns, cross-condition pattern changes, detection capability of finger movements from resting, and decoding performance of individual finger movements in comparison to classic mu/beta rhythms. These new features reveal some similar, but more different spatial and spectral patterns as compared with classic mu/beta rhythms. Decoding results further indicate that these new features (91%) can detect finger movements much better than classic mu/beta rhythms (75.6%). More importantly, these new features reveal discriminative information about movements of different fingers (fine body-part movements), which is not available in classic mu/beta rhythms. The capability in decoding fingers (and hand gestures in the future) from EEG will contribute significantly to the development of noninvasive brain computer interface (BCI) and neuroprosthesis with intuitive and flexible controls. |
| first_indexed | 2024-12-19T18:28:23Z |
| format | Article |
| id | doaj.art-6460b083fcf6443fa57338a99824a7be |
| institution | Directory Open Access Journal |
| issn | 1662-453X |
| language | English |
| last_indexed | 2024-12-19T18:28:23Z |
| publishDate | 2015-09-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj.art-6460b083fcf6443fa57338a99824a7be2022-12-21T20:10:47ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2015-09-01910.3389/fnins.2015.00308141679EEG Resolutions in Detecting and Decoding Finger Movements from Spectral AnalysisRan eXiao0Lei eDing1University of OklahomaUniversity of OklahomaMu/beta rhythms are well-studied brain activities that originate from sensorimotor cortices. These rhythms reveal spectral changes in alpha and beta bands induced by movements of different body parts, e.g. hands and limbs, in electroencephalography (EEG) signals. However, less can be revealed in them about movements of different fine body parts that activate adjacent brain regions, such as individual fingers from one hand. Several studies have reported spatial and temporal couplings of rhythmic activities at different frequency bands, suggesting the existence of well-defined spectral structures across multiple frequency bands. In the present study, spectral principal component analysis (PCA) was applied on EEG data, obtained from a finger movement task, to identify cross-frequency spectral structures. Features from identified spectral structures were examined in their spatial patterns, cross-condition pattern changes, detection capability of finger movements from resting, and decoding performance of individual finger movements in comparison to classic mu/beta rhythms. These new features reveal some similar, but more different spatial and spectral patterns as compared with classic mu/beta rhythms. Decoding results further indicate that these new features (91%) can detect finger movements much better than classic mu/beta rhythms (75.6%). More importantly, these new features reveal discriminative information about movements of different fingers (fine body-part movements), which is not available in classic mu/beta rhythms. The capability in decoding fingers (and hand gestures in the future) from EEG will contribute significantly to the development of noninvasive brain computer interface (BCI) and neuroprosthesis with intuitive and flexible controls.http://journal.frontiersin.org/Journal/10.3389/fnins.2015.00308/fullEEGBCInoninvasivePCASpectral featuresfine body-part movement |
| spellingShingle | Ran eXiao Lei eDing EEG Resolutions in Detecting and Decoding Finger Movements from Spectral Analysis Frontiers in Neuroscience EEG BCI noninvasive PCA Spectral features fine body-part movement |
| title | EEG Resolutions in Detecting and Decoding Finger Movements from Spectral Analysis |
| title_full | EEG Resolutions in Detecting and Decoding Finger Movements from Spectral Analysis |
| title_fullStr | EEG Resolutions in Detecting and Decoding Finger Movements from Spectral Analysis |
| title_full_unstemmed | EEG Resolutions in Detecting and Decoding Finger Movements from Spectral Analysis |
| title_short | EEG Resolutions in Detecting and Decoding Finger Movements from Spectral Analysis |
| title_sort | eeg resolutions in detecting and decoding finger movements from spectral analysis |
| topic | EEG BCI noninvasive PCA Spectral features fine body-part movement |
| url | http://journal.frontiersin.org/Journal/10.3389/fnins.2015.00308/full |
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