Classifying ECoG signals prior to voluntary movement onset

• Main Authors: Sang Hun Lee, Kyungin Choi, Sehyoon Jeong, Jun Sic Kim, Chun Kee Chung
• Format: Conference Proceeding
• Language: English
• Subjects: Accuracy; Brain-computer interface (BCI) prefrontal area; Brain-computer interfaces; Elbow; Electrodes; Feature extraction; Grasping; motor intention; motor preparation; Support vector machines; volunatry movement
• DOI: 10.1109/IWW-BCI.2013.6506622

Recently, in brain-computer interface (BCI) researches, earlier neural signals have allowed researchers to reduce the time gap between a subject's real action and the BCI response. The aims of this study were to use pre-movement signals to predict motor tasks, and to decide whether the prefrontal area, which has been recognized as generating premovement signals that reflect motor intention or preparation, generates useful pre-movement signals. Six patients with intractable epilepsy participated in this study and performed self-paced hand grasping and elbow flexion while electrocortico-graphy (ECoG) was recorded. The electrodes that showed clear power differences in a specific frequency band between two different movements were chosen at a preparatory stage (-2.0 s to 0 s). The average value of the squared power of the signal sample was extracted for the feature. A support vector machine (SVM) was used as a classifier. A total of twelve electrodes differentiating hand grasping and elbow flexion were selected. Four electrodes were placed on the prefrontal area. The average prediction rate was 74% (range, 55.4 to 99.3%) across the six subjects. The successful prediction of movement intention indicates that the prefrontal area may generate useful premovement signals and implies that our approach could produce BCI response faster than a subject's real actions.