Medical practitioners use portable electroencephalogram (EEG) headset to identify Neuro Developmental Disorders (NDD). However, the analysis of EEG signals gets affected as the recorded electrical activity always contaminated with artifacts. A portable EEG headset requires area-efficient hardware and an effective method to remove ocular artifacts. In this paper, a method is proposed, which removes ocular artifacts efficiently and consumes less hardware. This proposed method involves determining the appropriate decomposition level to obtain approximation coefficients (ACs) and detail coefficients (DCs) for minimal loss of useful information present in the EEG signal. In addition, an algorithm is also proposed to perform wavelet thresholding on wavelet coefficients using Local Maximal and Minimal (LMM) to remove artifacts from EEG signals without disturbing the information related to brain activity. The functionality of the proposed method is verified using MATLAB R2021a tool by computing average correlation coefficient & RMSE performance metrics and the proposed method is also modeled using Verilog HDL. The results show that the proposed DWT-LMM approach had superior performance in removing artifacts from EEG signals with the average correlation coefficient and RMSE values of 0.9369 and 2.2252, respectively. The functionality of this HDL netlist is verified by the VCS simulator and synthesized by Synopsys design compiler 32 nm UMC library cells. The proposed architecture layout is generated after placement & routing using Synopsys IC Compiler tool by employing 32 nm cell libraries and the results shows that the layout consumes 6490.07 μm2 of area and 792.18 μw of power with supply voltage of 0.95 V. Hence, the proposed ocular artifacts removal method and architecture can be employed in portable/wearable brain-computer interface (BCI) devices.
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