EEG Motor-Imagery BCI System Based on Maximum Overlap Discrete Wavelet Transform (MODWT) and Machine learning algorithm

Abstract

The ability of the human brain to communicate with its environment has become a reality through the use of a Brain-Computer Interface (BCI)-based mechanism. Electroencephalography (EEG) has gained popularity as a non-invasive way of brain connection. Traditionally, the devices were used in clinical settings to detect various brain diseases. However, as technology advances, companies such as Emotiv and NeuroSky are developing low-cost, easily portable EEG-based consumer-grade devices that can be used in various application domains such as gaming, education. This article discusses the parts in which the EEG has been applied and how it has proven beneficial for those with severe motor disorders, rehabilitation, and as a form of communicating with the outside world. This article examines the use of the SVM, k-NN, and decision tree algorithms to classify EEG signals. To minimize the complexity of the data, maximum overlap discrete wavelet transform (MODWT) is used to extract EEG features. The mean inside each window sample is calculated using the Sliding Window Technique. The vector machine (SVM), k-Nearest Neighbor, and optimize decision tree load the feature vectors.

Artificial Intelligence in Motor Imagery-Based Bci Systems: A Narrative

“…Preprocessing of acquired signals is necessary to reduce any noise or artefacts captured during the acquisition process. [4] These undesirable signals include the interference created when the device is mounted on the individual, EMG signals resulting from muscular contractions, and ocular artefact resulting from eye movement or blinking. This undesired noise in the EEG dataset will cause misinterpretation of the findings and incorrect conclusions; thus, they shall be filtered.…”

“…This process aims to filter the raw EEG data signals from the unwanted background noise. [4] The EEG signals are divided into different frequency bands such as "alpha, delta, beta, gamma, and theta" bands. The frequency range of motor imagery lies within the alpha or beta band range.…”

“…[4] The overall block diagram for the process is shown in Fig1and is concluded by reviewing different research papers.…”

The MODWT-ARIMA Model in Forecasting The COVID-19 Cases

“…. The following conditions must be fulfilled for the terms of the MODWT wavelet filter to be valid (Abdulwahab et al, 2021):…”