Abstract:
BACKGROUND:Motion-onset visual evoked potentials (mVEP) can provide a softer stimulus with reduced fatigue, and it has potential applications for brain computer interface(BCI)systems. However, the mVEP waveform is seriously masked in the strong background EEG activities, and an effective approach is needed to extract the corresponding mVEP features to perform task recognition for BCI control. NEW METHOD:In the current study, we combine deep learning with compressed sensing to mine discriminative mVEP information to improve the mVEP BCI performance. RESULTS:The deep learning and compressed sensing approach can generate the multi-modality features which can effectively improve the BCI performance with approximately 3.5% accuracy incensement over all 11 subjects and is more effective for those subjects with relatively poor performance when using the conventional features. COMPARISON WITH EXISTING METHODS:Compared with the conventional amplitude-based mVEP feature extraction approach, the deep learning and compressed sensing approach has a higher classification accuracy and is more effective for subjects with relatively poor performance. CONCLUSIONS:According to the results, the deep learning and compressed sensing approach is more effective for extracting the mVEP feature to construct the corresponding BCI system, and the proposed feature extraction framework is easy to extend to other types of BCIs, such as motor imagery (MI), steady-state visual evoked potential (SSVEP)and P300.
journal_name
J Neurosci Methodsjournal_title
Journal of neuroscience methodsauthors
Ma T,Li H,Yang H,Lv X,Li P,Liu T,Yao D,Xu Pdoi
10.1016/j.jneumeth.2016.11.002subject
Has Abstractpub_date
2017-01-01 00:00:00pages
80-92eissn
0165-0270issn
1872-678Xpii
S0165-0270(16)30268-0journal_volume
275pub_type
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