Fractal feature based detection of muscular and ocular artifacts in EEG signals

Electroencephalogram (EEG) is used to measure the bioelectric potential on the brain scalp. The recorded EEG signal can have different types of artifacts and the interpretation of a noisy EEG signal is difficult. In this research work, a simple method is proposed to minimize the artifacts present in the EEG signals recorded while perceiving a pure tone. The recorded EEG signal can contain artifacts, such as movement artifacts, muscle contraction artifacts and saturation artifacts. In the proposed method, fractal dimension based features with different interval length and time-domain based energy features were extracted from the EEG signals with and without simulated noise. Using the extracted features, neural network models were developed to classify the EEG signal as a normal or a noisy signal. Further, the performance of the model is also evaluated in terms of classification rate. From the results, it is observed that the neural network model developed with the combined fractal dimension features of interval length 2,3,4,5 and 6 with frame size 128 has the highest classification accuracy of 95.5%.