2015/05/06 by Rogerio Normand, Normand, Rogerio, Hugo Alexandre Ferreira +1
Biochemistry, Genetics and Molecular Biology · Neuroscience · #EEG and Brain-Computer Interfaces #FOS: Biological sciences #Neurons and Cognition (q-bio.NC) #Quantitative Methods (q-bio.QM) #q-bio.NC #q-bio.QM
paper · pdf · doi:10.48550/arxiv.1505.01228
5 pages, 3 figures with left/right images
openalex publication_date 2015/05/06 · arxiv created 2015/05/07 · arxiv updated 2015/05/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Electroencephalography (EEG) signals' interpretation is based on waveform analysis, where meaningful information should emerge from a plethora of data. Nonetheless, the continuous increase in computational power and the development of new data processing algorithms in the recent years have put into reach the possibility of analysing raw EEG signals. Bearing that motivation, the authors propose a new approach using raw data EEG signals and deep learning neural networks, for the classification of motor activities (executed and imagery). The hypothesis to be presented here is: each instantaneous measurement of the raw signal of all EEG channels (superchord) is unique per motor activity regardless the moment of measurement. This study has confirmed the hypothesis (results with accuracy over 80%, mean for 109 subjects), reinforcing the need of further research for the understanding of mental processes.