Brain Rhythm Sequencing Using EEG Signals: A Case Study on Seizure Detection

Abstract

A technique based on five brain rhythms (δ, θ, α, β, and γ) presented in the sequence for analyzing Electroencephalography (EEG) signals has been proposed. First, the production of the sequence has been accomplished by selecting the prominent brain rhythm having the maximum instantaneous power at specific timestamp consecutively throughout the EEG. To this purpose, the reassigned smoothed pseudo Wigner-Ville distribution (RSPWVD) has been employed. Then, in order to verify the proposed technique and evaluate its performance, a case study of seizure detection has been implemented. As experimental validation, 93 patients from the Karunya database have been investigated. Moreover, to characterize the brain rhythm sequence for seizure detection, two additional indices derived from the power discharge and synchronous behavior have been applied. Results show that the particular rhythm pattern during the seizure is usually one type (either δ, θ, or α) and it is subject-dependent. Hence, by focusing on the changes of such particular rhythm through the two indices, the time-related occurrences of seizures can be determined in detail. Meanwhile, the representative channels for seizure detection can be found by studying the similarity of sequences, which are helpful to reduce the number of applied channels. Finally, the proposed technique provides an accuracy of 98.9%, which demonstrates it is competent to detect the appearances of abnormal seizures from the EEG signals reliably. Consequently, the brain rhythm sequencing could open a new way to interpret and characterize the EEG in various applications such as for epileptic patients. INDEX TERMS Brain rhythm sequencing, electroencephalography (EEG), time-frequency analysis (TFA), reassigned smoothed pseudo Wigner-Ville distribution (RSPWVD), seizure detection.

An AIoT-Based Assistance System for Visually Impaired People

“…In the future, to further improve its detection accuracy with the help of sufficiently large data samples, several advanced models such as contrastive learning approach [52], self-attention enhanced deep residual network [53], time-series sequencing method [54], multiscale superpixelwise prophet model [55], and multistage stepwise discrimination with compressed MobileNet [56] will be investigated into the assistance system. Furthermore, additional functions, such as emotion recognition and fatigue detection, will be designed to enhance the overall life quality of visually impaired people.…”

Compact, Portable, High-Density Functional Near-Infrared Spectroscopy System for Brain Imaging

“…All or a on the specifications of the investigation, can be selected multiple devices can be used to image large brain regions. Other developed fNIRS devices may reach 128 channels, but the spatial and temporal characteristics of the presented system are way beyond those of the oth possibility of integrating EEG electrodes in the designed wearable device provides an additional advantage of multimodal imaging, a characteristic that employed for brain imaging [56,61].…”

An innovative EEG-based emotion recognition using a single channel-specific feature from the brain rhythm code method

“…Finally, in the previous study (Li et al, 2019), the seizure detection task using the BRS technique has been proposed. Although the EEG data are processed by BRS, the main objective and the classification method are entirely different, as the EEG recordings exhibit distinct properties in the two cases.…”

“…Following this way, brain rhythms can be presented in a sequential format based on timerelated variations, allowing for extracting code features for emotion recognition. Thus, this study designs the use of brain rhythm codes from three bases (δ, θ, α, β, or γ) of the sequences generated by the brain rhythm sequencing (BRS) technique previously proposed for seizure detection (Li et al, 2019). After that, four conventional machine learning classifiers, including k-NN, SVM, linear discriminant analysis (LDA), and logistic regression (LR), are evaluated for those extracted code features, with the aim of identifying the single optimal channel-specific feature and a suitable classifier for accomplishing satisfactory emotion recognition accuracies through the minimal data.…”