Advances in EEG-based detection of Major Depressive Disorder using shallow and deep learning techniques: A systematic review.

The contemporary diagnosis of Major Depressive Disorder (MDD) primarily relies on subjective assessments and self-reported measures, often resulting in inconsistent and imprecise evaluations. To address this issue and facilitate early intervention, there is a growing interest in utilizing objective criteria such as Electroencephalography (EEG) features analyzed through Artificial Intelligence (AI) techniques. This systematic review explores the advances in EEG-based detection of MDD using both shallow and deep learning methods, with the aim of enhancing understanding of the neural mechanisms underlying the disorder and identifying potential biomarkers for its diagnosis.

Advances in brain-computer interface for decoding speech imagery from EEG signals: a systematic review.

Numerous individuals encounter challenges in verbal communication due to various factors, including physical disabilities, neurological disorders, and strokes. In response to this pressing need, technology has actively pursued solutions to bridge the communication gap, recognizing the inherent difficulties faced in verbal communication, particularly in contexts where traditional methods may be inadequate. Electroencephalogram (EEG) has emerged as a primary non-invasive method for measuring brain activity, offering valuable insights from a cognitive neurodevelopmental perspective.

Advancements in automated diagnosis of autism spectrum disorder through deep learning and resting-state functional mri biomarkers: a systematic review.

Autism Spectrum Disorder(ASD) is a type of neurological disorder that is common among children. The diagnosis of this disorder at an early stage is the key to reducing its effects. The major symptoms include anxiety, lack of communication, and less social interaction.

A Heuristic Approach for Optical Transceiver Placement to Optimize SNR and Illuminance Uniformities of an Optical Body Area Network.

The bi-directional information transfer in optical body area networks (OBANs) is crucial at all the three tiers of communication, i.e., intra-, inter-, and beyond-BAN communication, which correspond to tier-I, tier-II, and tier-III, respectively.