A Review of EEG Signal Analysis for Diagnosis of Neurological Disorders using Machine Learning

Vandana Joshi (Login required)
Computer Engineering Department, Sarvajanik College of Engineering and Technology, Dr. R.K. Desai Marg,
Opp. Mission Hospital, Athwalines, Surat, Gujarat, India

Nirali Nanavati
Computer Engineering Department, Sarvajanik College of Engineering and Technology, Dr. R.K. Desai Marg,
Opp. Mission Hospital, Athwalines, Surat, Gujarat, India

Paper #3435 received 2 Jun 2021; revised manuscript received 15 Jul 2020; accepted for publication 11 Aug 2021; published online 7 Sep 2021.

DOI: 10.18287/10.18287/JBPE21.07.040201


Neurological disorders are diseases that affect the brain and the central autonomic nervous systems. These disorders take a huge toll on an individual's health and general well-being. After cardiovascular diseases, neurological disorders are the main cause of death. These disorders include epilepsy, Alzheimer’s disease, dementia, cerebrovascular diseases including stroke, migraine, Parkinson’s disease and numerous other disorders. This manuscript presents a state-of-the-art consolidated review of research on the diagnosis of the three most common neurological disorders using electroencephalogram (EEG) signals with machine learning techniques. The disorders discussed in this manuscript are the more prevalent disorders like epilepsy, Attention-deficit/hyperactivity disorder (ADHD), and Alzheimer’s disease. This manuscript helps in understanding the details about EEG signal processing for diagnosis and analysis of neurological disorders along with a discussion of the datasets, limitations, results and research scope of the various techniques.


neurological disorders; electroencephalogram (EEG); epilepsy; attention-deficit/hyperactivity disorder (ADHD); Alzheimer

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