Deep learning techniques applied to EEG data

Early yesterday morning I attended a remote conference talk, organized by ANT neuromeeting, and was fascinated by Dr. Sebastain Olbrich’s talk on his most recent paper on leveraging deep learning techniques applied to EEG data to help predict in conducting research. I go a bit into in on my YouTube (see below).

Curious to see if Dr. Olbrich would be interested for an interview on our channel. Will reach out to him and keep you posted!

In the meantime, here is the abstract of their paper:

Deep learning applied to electroencephalogram data in mental disorders: A systematic review

Mateo de Bardeci 1Cheng Teng Ip 2Sebastian Olbrich 3Affiliations expand

Free article

Abstract

In recent medical research, tremendous progress has been made in the application of deep learning (DL) techniques. This article systematically reviews how DL techniques have been applied to electroencephalogram (EEG) data for diagnostic and predictive purposes in conducting research on mental disorders. EEG-studies on psychiatric diseases based on the ICD-10 or DSM-V classification that used either convolutional neural networks (CNNs) or long -short-term-memory (LSTMs) networks for classification were searched and examined for the quality of the information they contained in three domains: clinical, EEG-data processing, and deep learning. Although we found that the description of EEG acquisition and pre-processing was sufficient in most of the studies, we found, that many of them lacked a systematic characterization of clinical features. Furthermore, many studies used misguided model selection procedures or flawed testing. It is recommended that the study of psychiatric disorders using DL in the future must improve the quality of clinical data and follow state of the art model selection and testing procedures so as to achieve a higher research standard and head toward a clinical significance.

Keywords: CNN; Deep learning; Electroencephalogram; LSTM; Mental disorders.


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