Comparing Common Average Referencing to Laplacian Referencing in Detecting Imagination and Intention of Movement for Brain Computer Interface
Brain-computer interface (BCI) is a paradigm that offers an alternative communication channel between neural activity generated in the brain and the user’s external environment. This paper investigates detection of intention of movement from surface EEG during actual and imagination of movement whic...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2017-01-01
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| Series: | MATEC Web of Conferences |
| Online Access: | https://doi.org/10.1051/matecconf/201714001028 |
| _version_ | 1818642806186442752 |
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| author | Syam Syahrull Hi-Fi Lakany Heba Ahmad R.B. Conway Bernard A. |
| author_facet | Syam Syahrull Hi-Fi Lakany Heba Ahmad R.B. Conway Bernard A. |
| author_sort | Syam Syahrull Hi-Fi |
| collection | DOAJ |
| description | Brain-computer interface (BCI) is a paradigm that offers an alternative communication channel between neural activity generated in the brain and the user’s external environment. This paper investigates detection of intention of movement from surface EEG during actual and imagination of movement which is essential for developing non-invasive BCI system for neuro-impaired patients. EEG signal was recorded from 11 subjects while imagining and performing right wrist movement in multiple directions using 28 electrodes based on international 10-20 standard electrode placement locations. The recorded EEG signal later was filtered and pre-processed by spatial filter namely; Common average reference (CAR) and Laplacian (LAP) filter. Features were extracted from the filtered signal using ERSP and power spectrum and classified by k-nearest neighbour (k-NN) and quadratic discriminant analysis (QDA) classifiers. The classification results show that LAP filter has outperformed CAR with respect to classification. Classification accuracy ranged from 63.33% to 100% for detection of imagination of movement and 60% to 96.67% for detection of intention of actual movement. In both of detection of imagination and intention of movement k-NN classifier gave better result compared to QDA classifier. |
| first_indexed | 2024-12-16T23:48:54Z |
| format | Article |
| id | doaj.art-f4df9f2980e84bf4b2a740f60cf25843 |
| institution | Directory Open Access Journal |
| issn | 2261-236X |
| language | English |
| last_indexed | 2024-12-16T23:48:54Z |
| publishDate | 2017-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | MATEC Web of Conferences |
| spelling | doaj.art-f4df9f2980e84bf4b2a740f60cf258432022-12-21T22:11:23ZengEDP SciencesMATEC Web of Conferences2261-236X2017-01-011400102810.1051/matecconf/201714001028matecconf_iceesi2017_01028Comparing Common Average Referencing to Laplacian Referencing in Detecting Imagination and Intention of Movement for Brain Computer InterfaceSyam Syahrull Hi-FiLakany HebaAhmad R.B.Conway Bernard A.Brain-computer interface (BCI) is a paradigm that offers an alternative communication channel between neural activity generated in the brain and the user’s external environment. This paper investigates detection of intention of movement from surface EEG during actual and imagination of movement which is essential for developing non-invasive BCI system for neuro-impaired patients. EEG signal was recorded from 11 subjects while imagining and performing right wrist movement in multiple directions using 28 electrodes based on international 10-20 standard electrode placement locations. The recorded EEG signal later was filtered and pre-processed by spatial filter namely; Common average reference (CAR) and Laplacian (LAP) filter. Features were extracted from the filtered signal using ERSP and power spectrum and classified by k-nearest neighbour (k-NN) and quadratic discriminant analysis (QDA) classifiers. The classification results show that LAP filter has outperformed CAR with respect to classification. Classification accuracy ranged from 63.33% to 100% for detection of imagination of movement and 60% to 96.67% for detection of intention of actual movement. In both of detection of imagination and intention of movement k-NN classifier gave better result compared to QDA classifier.https://doi.org/10.1051/matecconf/201714001028 |
| spellingShingle | Syam Syahrull Hi-Fi Lakany Heba Ahmad R.B. Conway Bernard A. Comparing Common Average Referencing to Laplacian Referencing in Detecting Imagination and Intention of Movement for Brain Computer Interface MATEC Web of Conferences |
| title | Comparing Common Average Referencing to Laplacian Referencing in Detecting Imagination and Intention of Movement for Brain Computer Interface |
| title_full | Comparing Common Average Referencing to Laplacian Referencing in Detecting Imagination and Intention of Movement for Brain Computer Interface |
| title_fullStr | Comparing Common Average Referencing to Laplacian Referencing in Detecting Imagination and Intention of Movement for Brain Computer Interface |
| title_full_unstemmed | Comparing Common Average Referencing to Laplacian Referencing in Detecting Imagination and Intention of Movement for Brain Computer Interface |
| title_short | Comparing Common Average Referencing to Laplacian Referencing in Detecting Imagination and Intention of Movement for Brain Computer Interface |
| title_sort | comparing common average referencing to laplacian referencing in detecting imagination and intention of movement for brain computer interface |
| url | https://doi.org/10.1051/matecconf/201714001028 |
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