Denoising of EEG signals using Discrete Wavelet Transform based Scalar Quantization

Efficient transmission of EEG signals is necessary to optimize the performance of Brain Computer Interface (BCI) applications. This can be achieved by improving the signal to noise ratio of EEG signals. There are various methods employed in denoising such signals. Since Bio medical signals contains more redundancies it is also necessary to compress the EEG signals. For that quantization based compression schemes are used which includes complexity in the circuit. Hence Discrete Wavelet transform based Scalar Quantization (DWTSQ) is used for improving the Signal to Noise Ratio (SNR) with reduced complexity has been proposed in this project. The hybridization of quantizer with DWT transform reduces number of bits needed for storing the transmitted coefficients. The Denoised signal is classified using Immune Feature Weighted Support Vector Machine(IFWSVM) for measuring the accuracy of EEG features.. This DWTSQ increases the SNR of the transmitted signal and also produces better accuracy for different features. This is simulated using MATLAB software which gives better result than other techniques used today.