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ECoG signal processing for Brain Computer Interface with multiple degrees of freedom for clinical application

Abstract : Brain-Computer Interfaces (BCI) are systems that allow severely motor-impaired patients to use their brain activity to control external devices, for example upper-limb prostheses in the case of motor BCIs. The user's intentions are estimated by applying a decoder on neural features extracted from the user's brain activity. Signal processing challenges specific to the clinical deployment of motor BCI systems are addressed in the present doctoral thesis, namely asynchronous mono-limb or sequential multi-limb decoding and accurate decoding during active control states. A switching decoder, namely a Markov Switching Linear Model (MSLM), has been developed to limit spurious system activations, to prevent parallel limb movements and to accurately decode complex movements.The MSLM associates linear models with different possible control states, e.g. activation of a specific limb, specific movement phases. Dynamic state detection is performed by the MSLM, and the probability of each state is used to weight the linear models. The performance of the MSLM decoder was assessed for asynchronous wrist and multi-finger trajectory reconstruction from electrocorticographic signals. It was found to outperform previously reported decoders for the limitation of spurious activations during no-control periods and permitted to improve decoding accuracy during active periods.
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Submitted on : Wednesday, April 11, 2018 - 10:30:07 AM
Last modification on : Wednesday, November 4, 2020 - 3:13:48 PM


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  • HAL Id : tel-01763451, version 1



Marie-Caroline Schaeffer. ECoG signal processing for Brain Computer Interface with multiple degrees of freedom for clinical application. Medical Physics []. Université Grenoble Alpes, 2017. English. ⟨NNT : 2017GREAS026⟩. ⟨tel-01763451⟩



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