Finger flexion imagery: EEG classification through physiologically-inspired feature extraction and hierarchical voting

• Published in: 2016 4th International Winter Conference on Brain-Computer Interface (BCI) pp. 1 - 4
• Main Authors: Furman, Daniel, Reichart, Roi, Pratt, Hillel
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.02.2016
• Subjects: BMI; Decision system; EEG; Electrodes; Electroencephalography; Feature extraction; Motor imagery; Multi-class SVM; Spatiotemporal features; Spectral features; Spirals; Support vector machines; Thumb
• ISBN: 1467378410; 9781467378413
• DOI: 10.1109/IWW-BCI.2016.7457445

Accurate electroencephalography (EEG) classification of finger flexion imagery would endow non-invasive brainmachine interfaces (BMIs) with a much richer control repertoire. Traditionally, it has been assumed that non-invasive methods lack the resolution required for success on such a fine discrimination task. In this study, we challenged this assumption. EEG was acquired while subjects imagined performing individual and bimanual finger flexions. A new method of spatiotemporal and spectral feature extraction was applied, and multi-class support vector machine (SVM) classifiers were trained. Predictions and probabilities then served as inputs to a novel voting scheme, which output the system decision. The present approach achieved a mean population (n=15) accuracy of 30.86±1.76%, nearly twice the chance guessing level (16.71±1.68%) for the six-class task evaluated. Finger imagery is thus shown to be classifiable through EEG analysis alone.