Deep Convolutional Neural Network for Motor Imagery EEG Classification
Prachi Dwivedi, Ashish Dubey
BCI (Brain-computer interface), EEG (Electroencephalogram), CNNs (Convolutional Neural Networks), DCNN Model.
This research is based on the Deep Convolutional Neural Networks (DCNN) approach to the Detection of Motor Imagery (MI) tasks in EEG (Electroencephalogram), the use of brain-computer interface (BCI) techniques for direct communication between the human body and outside the world, which has significant forecast applications in the field of cognitive science & medical rehabilitation. The technology of DL (Deep Learning) has produced noteworthy results in BCI systems in recent years, in particular through the use for recognition and interpretation in MI of CNNs (Convolutional Neural Networks). The proposed procedure converts input EEG signals first into images by applying images of MI tasks EEG signals to DCNN level after a time-frequency transformation. For image classification, we train DCNN models. The efficiency of the proposed method is assessed on the IV dataset of BCI competition III. Evaluation metrics like the proposed accuracy method results quantitatively. Findings reveal that the 99.93% accuracy value of the proposed system is the highest one among existing accuracy scores.
Article Details
Unique Paper ID: 152824

Publication Volume & Issue: Volume 8, Issue 4

Page(s): 510 - 519
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