Removal of muscle artifacts from EEG recordings of spoken language production.
Research on the neural basis of language processing has often avoided investigating spoken language production by fear of the electromyographic (EMG) artifacts that articulation induces on the electro-encephalogram (EEG) signal. Indeed, such articulation artifacts are typically much larger than the brain signal of interest. Recently, a Blind Source Separation technique based on Canonical Correlation Analysis was proposed to separate tonic muscle artifacts from continuous EEG recordings in epilepsy. In this paper, we show how the same algorithm can be adapted to remove the short EMG bursts due to articulation on every trial. Several analyses indicate that this method accurately attenuates the muscle contamination on the EEG recordings, providing to the neurolinguistic community a powerful tool to investigate the brain processes at play during overt language production.
De Vos
Maarten
M
Department of Electrical Engineering (ESAT), Katholieke Universiteit Leuven, Leuven, Belgium. maarten.devos@gmail.com
Vos
De Maarten
de M
Riès
Stephanie
S
Vanderperren
Katrien
K
Vanrumste
Bart
B
Alario
Francois-Xavier
FX
Van Huffel
Sabine
S
Huffel
Van Sabine
VS
Burle
Boris
B
D000328:Adult; D000465:Algorithms; D016477:Artifacts; D001921:Brain / Q000502:physiology*; D004569:Electroencephalography / Q000379:methods*; D004576:Electromyography / Q000379:methods; D005071:Evoked Potentials; D005260:Female; D006801:Humans; D007802:Language; D008297:Male; D018482:Muscle, Skeletal / Q000502:physiology*; D012815:Signal Processing, Computer-Assisted; D013060:Speech / Q000502:physiology*; D013997:Time Factors; D055815:Young Adult