Electroencephalographic (EEG) based Deep Learning (DL): A Comparative Review
Keywords:
Electroencephalogram, Deep learning, convolution Neural Networks, Datasets, stimuliAbstract
Deep learning (DL) has recently shown great promise in supporting knowledge of electroencephalographic (EEG) as a result of its ability to discover visual features (feature representation) from original (raw) data. This review will look at the latest developments in the research area of the EEG by analyzing a largest amount of the recent and definitive publications on EEG based on DL for biometrics identification. It covers the latest developments in different parts of the DL-EEG methodology and offers valuable information about them in order to improve its implementation. Also, it will provide interested researchers with a brief overview of the prospects of applying DL typical EEG processing methods. In addition to highlighting interesting methods and trends that used to acquisition and analyses brain signals, the stimulations, feature extractions and classifications. We summarize our review in some recommendations and proposals in the hope of promoting effective viable research in this field. We have highlighted interesting approaches and directions from this extensive research in order to provide ample information for future research. This review revealed that the duration of time spent trying to collect EEG data ranged from (10) minutes or less to a long time of hours, and interestingly, we found that more than 50 percent of the research design their models using publicly available datasets. Furthermore, There about half of the researchers used unprocessed or preprocessed EEG samples to train their models. Compared to traditional approaches, DL had an improvement in accuracy of 4% among the most applicable studies. More importantly, we discovered that the majority of previous studies have poor reproducibility: it is extremely difficult or impossible to replicate the majority of research due to a lack of data and codes. The importance of the paper lies in helping the research community to share and develop work more effectively. And We'll also offer a list of suggestions for more studies in the future.
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