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Session: Poster session II
Session starts: Thursday 24 January, 16:00

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Meron Vermaas (Radboud University Nijmegen)
Jinne Geelen (TU Delft)

Abstract:
Our understanding of the origin of measured brain signals should be extended to improve signal decoding used in brain-computer interfaces (BCI). Finger movements are a promising candidate to control electrocorticography (ECoG) based BCI. A recent functional MRI study demonstrated the somatotopic organization of finger representation on the sensorimotor cortex. Activity in the sensorimotor cortex detected with fMRI relates to activity in high-density ECoG grids. Thus, the activity patterns underlying ECoG potentials are expected to be related to individual finger movements. The goal of this study is to estimate the independent source activity related to individual finger movements somatotopically mapped on the sensorimotor cortex. The ECoG data of one human participant is obtained with a high-density grid (32 electrodes) above the sensorimotor cortex during a finger flexion task. Finger flexion is measured during the task with a data glove. An independent component analysis (ICA) is performed and the optimal number of components is investigated. The components retrieved from the ICA are correlated to the finger flexion data. Probability maps of the components are computed to check the stability of the ICA outcome. The spatial spread of the components over the HD grid is examined and compared to the expected activity pattern based on a volume conduction model. The successful application of ICA on HD ECoG grids in this exploratory study could be an indication for the feasibility of inverse modelling and source reconstruction.