Abstract
Brain Computer Interface (BCI) is a major research field that is based upon Electroencephalography (EEG) brain signals, which are captured using EEG electrodes, amplified and filtered before being converted to the digital form in order to perform thorough pre-processing and machine-learning. In this study, the design and implementation of the BCI control and monitoring system for smart home automation using wavelet features, which is based upon a dual-channel analogue EEG signal acquisition module is reported. The designed analogue EEG module performs EEG signal acquisition, signal amplification and filtering. Although the EEG data set contains thousands of samples and more than 15 different classes, we limit our study on 226 samples grouped into seven classes with 8-second time duration per sample. With careful settings of deep-learning classifier model parameters, the training and testing were successful with high accuracy results. The designed BCI system has several advantages including a large bandwidth of 400 Hz, low number of EEG electrodes, easy setup, simple user interface, pre-processing and digital filtering, fast machine learning, multi-class identification, monitoring and control models, high classification accuracy and low cost. This research work provided several contributions including the creation of recent and original EEG data set using well-labelled recordings at an adequate sampling rate of 2 kHz. The EEG signal acquisition module with 400-Hz bandwidth provides precise and rich EEG signal information needed for feature extraction. Our results are reproducible and have been tested and deployed on Raspberry pi 4 with Python. The designed wavelet-based BCI system consists of analogue EEG signal acquisition and machine-learning modules, which consist of deep-learning Multi-layer perceptron (MLP) classifiers and linear discriminant analysis (LDA) as well as other classifier models for comparison including convolutional neural networks (CNN). The deep learning and LDA classifiers models produced the best performance with average accuracy of 95.6% and 96% for both training and testing data sets.