Energy-Efficient Electroencephalogram Signal Classification with Artificial Neural Networks and Fast Fourier Transform

In the realm of brain-computer interfaces (BCIs), the demand for energy-efficient algorithms to accurately classify electroencephalogram (EEG) signals has surged. This research presents an innovative approach to electroencephalography signal classification employing Artificial Neural Networks (ANNs) in tandem with the Fast Fourier Transform (FFT), specifically tailored for implementation in Verilog and Xilinx 14.7 environments. The methodology commences with preprocessing raw electroencephalography data to ameliorate signal quality by mitigating noise and artifacts. Following this, the fast fourier transform is judiciously applied to transform electroencephalography signals from the time domain into the frequency domain, thereby extracting salient spectral features.This frequency-domain representation is subsequently employed as input for a meticulously engineered artificial neural network architecture, which is trained to discern electroencephalography patterns associated with distinct mental states or commands. A pivotal focus of this method is the optimization of the artificial neural network model, ensuring a judicious trade-off between classification accuracy and computational efficiency, specifically tailored for the Verilog and Xilinx 14.7 platforms. Experimental results, utilizing a diverse electroencephalography dataset encompassing various cognitive tasks, underscore the efficacy of the proposed approach in achieving commendable classification accuracy while adhering to stringent power constraints.