Gustatory stimulus-based electroencephalogram signal classification

Brain computer interface (BCI) gives a prompt correspondence between human brain and personal computer (PC) and makes an interpretation for controlling the outside gadgets. Taste composition (TASCO)-based EEG signal classification is used to differentiate normogeusia and hypogeusia. EEG signal of TASCO is pre-processed by utilising FIR band pass channel to mitigate the artefacts of noise. In this proposed work, the discrete wavelet transform (DWT) is used as the feature extraction method. DWT breaks down the separated EEG signal into its related frequency bands and the measurable features of the detailed coefficient of the alpha wave are analysed in time domain. The extracted features like mean absolute value (MAV) and variance are classified using a multilayer perceptron neural network classifier which provides high accuracy. In this paper, sour TASCO is analysed to identify the gall bladder problem in a human and improve the accuracy of the system as much as 95% compared to conventional methods.

Automated summary

The Brain-Computer Interface provides real-time communication between the human brain and a personal computer for controlling external devices. This study utilizes taste composition-based EEG signal classification to differentiate between normal and reduced taste sensitivity. The EEG signal is pre-processed using FIR band pass filtering to reduce noise interference. The proposed method employs Discrete Wavelet Transform for feature extraction, analyzing alpha wave coefficients in the time domain to achieve high accuracy in gall bladder problem identification.