Manas P. Patil,
V.G. Raut,
Krushna S. Rajkule,
Rutuja M. Dusane,
- Student, Sinhgad College of Engineering, Pune, Maharashtra, India
- Assistant Professor, Sinhgad College of Engineering, Pune, Maharashtra, India
- Student, Sinhgad College of Engineering, Pune, Maharashtra, India
- Student, Sinhgad College of Engineering, Pune, Maharashtra, India
Abstract
A Brain-Computer Interface (BCI) is a system that translates brain activity patterns into computer commands, bypassing physical movement. Electroencephalography (EEG) is commonly used to acquire signals in BCI research. Visual evoked potentials (VEPs) are brain responses in the visual cortex to visual stimuli. Recent studies show that exposing individuals to flickering at a consistent frequency generates EEG signals synchronized with the stimulation. Efficient extraction of VEP signals begins with preprocessing raw EEG data. Various machine learning techniques—such as support vector machines (SVMs), recurrent neural networks (RNNs), and convolutional neural networks (CNNs)—are evaluated for their ability to classify VEP components like the P100 wave. Feature engineering methods tailored to VEP characteristics are explored to enhance model performance. This study emphasizes integrating machine learning with preprocessed EEG features from Steady State Visually Evoked Potential (SSVEP) signals flickering at specific frequencies. The dataset includes EEG data from experiments using repetitive visual stimuli with two distinct flicker frequencies. The goal is to identify and implement a suitable machine learning approach that improves the extraction of valuable information from VEP data, facilitating further research into these systems.
Keywords: Brain Computer Interface (BCI) Electroencephalography (EEG), Steady State Visually Evoked Potential (SSVEP), visual stimuli
[This article belongs to Journal of Microwave Engineering and Technologies(jomet)]
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Journal of Microwave Engineering and Technologies
| Volume | 11 |
| Issue | 02 |
| Received | July 5, 2024 |
| Accepted | July 15, 2024 |
| Published | August 14, 2024 |