1e416013186c20a17cb4dcc 20230720050918124 beie:beie director@blueeyesintelligence.org WEB-FORM International Journal of Recent Technology and Engineering (IJRTE) IJRTE 22773878 10.35940/ijrte.2277-3878 https://www.ijrte.org/ 07 30 2023 12 2 Department of Electronics and Communication Engineering, SJB Institute of Technology, Bangalore (Karnataka), India. Mr. Darshan B D https://orcid.org/0000-0002-0749-8302 Vyshnavi Shekhar B S https://orcid.org/0009-0007-7754-2747 Department of Electronics and Communication Engineering, SJB Institute of Technology Bangalore (Karnataka), India. Meghana M Totiger https://orcid.org/0009-0007-2786-9127 Department of Electronics and Communication Engineering, SJB Institute of Technology Bangalore (Karnataka), India. Priyanka N https://orcid.org/0009-0000-1069-8586 Department of Electronics and Communication Engineering, SJB Institute of Technology Bangalore (Karnataka), India. Spurthi A https://orcid.org/0009-0009-0475-6538 Department of Electronics and Communication Engineering, SJB Institute of Technology Bangalore (Karnataka), India. An electroencephalograph is a device that records all electrical energy in the human brain using wearable metal electrodes placed on the skull. Electrical impulses connect brain cells and are always mobile, even at rest. This activity appears as a squiggly line in EEG recordings. Activity gaze data is pre-processed to a frequency range of 0 to 75 Hz. This creates a new matrix with a sample rate of 200 Hz and a range of 0-75 Hz. A finite-impulse-response low-pass filter was used because the bandpass would distort his EEG data after processing. Each pre-processed EEG signal has an output, which completes feature extraction. Principal Component Analysis or PCA is passed in the feature reduction phase. PCA is an analytical process that uses singular value decomposition to transform a collection of corresponding features into mutually uncorrelated features or principal components. Principal component analysis: (a) mean normalization of features (b) covariance matrix (c) eigenvectors (d) reduced features or principal components. The above steps are passed to the SVM classifier for sentiment output. His VHDL code and testbench for 2*2 matrices were written, waveforms and RTL schemes were created in Xilinx 14.5. For the FPGA implementation, a Simulink model was designed, and the eigenvalues were pre-determined using a system generator. 07 30 2023 102 109 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true International Journal of Recent Technology and Engineering (IJRTE) No, I did not receive. No conflicts of interest to the best of our knowledge. No, the article does not require ethical approval and consent to participate with evidence. Not relevant. All authors having equal contribution for this article. 10.35940/ijrte.B7808.0712223 https://www.ijrte.org/portfolio-item/B78080712223/