Decoding finger flexion from band-specific ECoG signals in humans

Nanying Liang 1 Laurent Bougrain 1
1 CORTEX - Neuromimetic intelligence
Inria Nancy - Grand Est, LORIA - AIS - Department of Complex Systems, Artificial Intelligence & Robotics
Abstract : This article presents the method that won the brain-computer interface (BCI) competition IV addressed to the prediction of the finger flexion from electrocorticogram (ECoG) signals. ECoG-based BCIs have recently drawn the attention from the community. Indeed, ECoG can provide higher spatial resolution and better signal quality than classical EEG recordings. It is also more suitable for long-term use. These characteristics allow to decode precise brain activities and to realize efficient ECoG-based neuroprostheses. Signal processing is a very important task in BCIs research for translating brain signals into commands. Here, we present a linear regression method based on the amplitude modulation of band-specific ECoG including a short-term memory for individual finger flexion prediction. The effectiveness of the method was proven by achieving the highest value of correlation coefficient between the predicted and recorded finger flexion values on data set 4 during the BCI competition IV.
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Article dans une revue
Frontiers in Neuroscience, Frontiers, 2012, 6 (91), 〈10.3389/fnins.2012.00091〉
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https://hal.inria.fr/hal-00762316
Contributeur : Laurent Bougrain <>
Soumis le : jeudi 6 décembre 2012 - 23:16:56
Dernière modification le : jeudi 11 janvier 2018 - 06:25:23

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Nanying Liang, Laurent Bougrain. Decoding finger flexion from band-specific ECoG signals in humans. Frontiers in Neuroscience, Frontiers, 2012, 6 (91), 〈10.3389/fnins.2012.00091〉. 〈hal-00762316〉

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