Classification of Emotion using Eeg Signals: an FPGA Based Implementation
Darshan B D1, Vyshnavi Shekhar B S2, Meghana M Totiger3, Priyanka N4, Spurthi A5
1Mr. Darshan B D, Department of Electronics and Communication Engineering, SJB Institute of Technology, Bangalore (Karnataka), India.
2Vyshnavi Shekhar B S, Department of Electronics and Communication Engineering, SJB Institute of Technology Bangalore (Karnataka), India.
3Meghana M Totiger, Department of Electronics and Communication Engineering, SJB Institute of Technology Bangalore (Karnataka), India.
4Priyanka N, Department of Electronics and Communication Engineering, SJB Institute of Technology Bangalore (Karnataka), India.
5Spurthi A, Department of Electronics and Communication Engineering, SJB Institute of Technology Bangalore (Karnataka), India.
Manuscript received on 07 June 2023 | Revised Manuscript received on 01 July 2023 | Manuscript Accepted on 15 July 2023 | Manuscript published on 30 July 2023 | PP: 102-109 | Volume-12 Issue-2, July 2023 | Retrieval Number: 100.1/ijrte.B78080712223 | DOI: 10.35940/ijrte.B7808.0712223
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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.
Keywords: Electroencephalography (EEG), Autonomous Nervous System (ANS), Principal Component Analysis (PCA), Support Vector Machine (SVM).
Scope of the Article: Classification