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EEG Classification by factoring in Sensor Configuration
[article]
2020
arXiv
pre-print
Electroencephalography (EEG) serves as an effective diagnostic tool for mental disorders and neurological abnormalities. Enhanced analysis and classification of EEG signals can help improve detection performance. A new approach is examined here for enhancing EEG classification performance by leveraging knowledge of spatial layout of EEG sensors. Performance of two classification models - model 1 that ignores the sensor layout and model 2 that factors it in - is investigated and found to achieve
arXiv:1905.09472v2
fatcat:4tya2ttmrjdq3oxq72ryosmyae