Seminar Report on An improved feature extraction algorithms of EEG signals

Seminar Report on An improved feature extraction algorithms of EEG signals

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  An Improved FeatureExtraction Algorithm of EEG Signals A Colloquim report submitted for End Semester Evaluation NATIONAL INSTITUTE OF TECHNOLOGY SILCHAR Guided by -Dr. Koushik Guha
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  Introduction Electroencephalogram (EEG) isa technique for recording electrical activity of the brain using electrodes in scalp . It has become a widely used tool in neuroengineering, cognitive neuroscience, and brain-computer interface (BCI) systems due to its high temporal resolution (millisecond-level response). EEG signals generally have: Low amplitude (10–100 µV), Frequency range: 0.5–100 Hz, Non-stationary behavior, Sensitivity to external and internal noise (e.g., muscle artifacts, eye movement). Types of EEG frequency bands: Delta (0.5–4 Hz) Theta (4–8 Hz) Alpha (8–12 Hz) Beta (12–30 Hz) Gamma (30–100 Hz) These bands are crucial for decoding cognitive states and motor imagery in BCI applications. Brain-Computer Interface (BCI) aims to connect the human brain with external systems, such as wheelchairs or robotic arms, by converting neural signals into control commands. The key challenge is robust feature extraction from noisy EEG data.
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  Methodology To enhance theclassification accuracy of motor imagery EEG signals, we use a three- stage pipeline: 1.Independent Component Analysis (ICA) 2.Wavelet Transform (WT) 3.Common Spatial Pattern (CSP) 2.1 Independent Component Analysis (ICA) ICA is a blind source separation technique that separates observed signals into statistically independent components. It is used for artifact removal, especially eliminating EOG and ECG noise. Mathematical Formulation: X(t)=A⋅S(t)⇒S(t)=W⋅X(t) Where: X(t): Observed EEG signal matrix A: Mixing matrix S(t): Source signals W=A -Unmixing matrix −1 ICA assumes that the sources are independent and non-Gaussian, making it well-suited for EEG signal decomposition. Fig: The Mathemetical model of ICA .[1]
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  2.2 Wavelet Transform(WT) Wavelet Transform enables time-frequency analysis of non-stationary signals. It decomposes signals into wavelet domain , maipulating the signal and transform it back. Steps: 1.Apply discrete wavelet transform to EEG signals. 2.Threshold wavelet coefficients to remove noise. 3.Reconstruct signals using inverse WT. High-frequency coefficients are often associated with noise, while low-frequency coefficients preserve the core EEG signal. 2.3 Common Spatial Pattern (CSP) CSP is a supervised feature extraction technique widely used in motor imagery tasks. CSP extracts meaningful features from the cleaned signal, helping distinguish between different mental states with high accuracy. 3. Proposed Workflow The full workflow includes the following: Step 1: Apply ICA to remove noise and artifacts. Step 2: Perform Wavelet Transform on each component. Step 3: Apply thresholding and inverse WT to clean the signal. Step 4: Apply CSP to extract meaningful features. Step 5: Use machine learning (e.g., Bagging Tree) for classification. Fig 2: The process of the propsed method. [1]
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  5. Results andDiscussion 5.1 ICA Effectiveness ICA successfully separates artifact-related components from neural activity, improving signal quality and reducing false detections. 5.2 WT Improvement WT preserves relevant neural components while discarding noise. Wavelet thresholding allows selective removal of high-frequency noise without affecting low-frequency ERD/ERS patterns. 4. Dataset Description Source: BCI Competition Dataset (109 participants) Task: Motor imagery (left hand, right hand, feet) Electrodes: 64-channel cap (10-20 system), using BCI2000 platform Trials: ~1500 total recordings Each trial involves a visual cue followed by a motor imagery task. The dataset is balanced across classes. Fig 3: Electrodes arrangement position.[1]
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  5.3 CSP Extraction TheCSP-filtered signal emphasizes patterns that are distinct between motor tasks by enhancing the differences in EEG signal pattern. 5.4 Classification Accuracy The use of a Bagging Tree classifier provides robustness through ensemble learning. It combines multiple decision trees trained on bootstrapped datasets. Fig 4: Bagging Tree 6.Future Scope Deep Learning Models: Explore CNNs and LSTMs for end-to-end feature learning. Online BCI: Adapt the method for real-time processing and control. Multi-class Tasks: Extend to multi-class motor imagery (e.g., left, right, feet, tongue).
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  Conclusion The proposed methodintegrates ICA, Wavelet Transform, and CSP to form an efficient EEG signal feature extraction pipeline. This hybrid approach enhances signal quality and improves classification accuracy, making it suitable for motor imagery- based BCI applications. Future work can explore real-time implementation and deep learning-based classifiers to further improve performance. References 1.[1] X. Geng, D. Li, H. Chen, P. Yu, H. Yan, M. Yue, “An improved feature extraction algorithms of EEG signals based on motor imagery brain-computer interface,” Alexandria Engineering Journal, vol. 61, no. 6, pp. 4807-4820, 2022. DOI: 10.1016/j.aej.2021.10.034. 2.[2] “Feature Extraction from EEG Signals: A Deep Learning Perspective,” Int. J. for Modern Trends in Science and Technology, vol. 9, no. 08, pp. 45–50, 2023. DOI: 10.46501/IJMTST0908008. 3.[3] A. Mohammad, F. Siddiqui, M. A. Alam, “Feature Extraction from EEG Signals: A deep learning perspective,” in Proc. 2021 Int. Conf. on Cloud Computing, Data Science & Engineering (Confluence), Noida, India, 2021, pp. 757-760. DOI: 10.1109/Confluence51648.2021.9377108. 4.[4] Z. Ling, C. Shuyue, S. Yuqiang, M. Zhe, “Extraction of Evoked Related Potentials by using the Combination of Independent Component Analysis and Wavelet Analysis,” J. Biomed. Eng., vol. 27, no. 4, pp. 741–745, 2010. 5.[5] “International Conference on Computational Intelligence and Networks,” IEEE, 2016, pp. 84-89.