Sharing a summary of below paper on DL in EEG. This paper has reviewed 90 published papers and provides a workflow diagram of DL in EEG classification. For those interested in using EEG data in deep learning techniques, this diagram seems like a great starting point to help determine what works best for your goals and objectives of your research or project.
“Task-specific deep learning recommendation diagram. The workflow begins with task type (with connected boxes indicating the task’s general deep learning architecture recommendation) and leads into deep learning architecture characteristic recommendations, which can serve as the starting point for designing deep learning architectures in future research.”
How I used some of the findings of my dissertation to compose electronic music (using Ableton Live) to help steer brainwaves towards specific frequencies found to be detected in the brain when we seeking knowledge and information.
Overview of the thesis paper. What happens with our brainwaves when we are curious about things and look for knowledge and information? What types of brainwaves do we emanate when we seek information? And do these waves change when we feel positive vs negative before seeking knowledge?
Dr. Sebastian Olbrich, chief of psychiatry university hospital of Switzerland, joined us to today to share with his amazing research and findings in using EEG data in deep learning, the pitfalls, as well as best practices for young aspired scientists interested in EEG data.
Full video on YouTube 👇
References: Deep learning applied to electroencephalogram data in mental disorders: A systematic review – Mateo de Bardeci 1, Cheng Teng Ip 2, Sebastian Olbrich 3 Affiliations expand PMID: 33991592 DOI: 10.1016/j.biopsycho.2021.108117
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
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.