Abstract
After promising results of deep learning in numerous fields, researchers have started exploring Electroencephalography (EEG) data for human behaviour and emotion recognition tasks that have a wide range of practical applications. But it has been a huge challenge to study EEG data collected in a non-invasive manner due to its heterogeneity, vulnerability to various noise signals and variant nature to different subjects and mental states. Though several methods have been applied to classify EEG data for the aforementioned tasks, multi-class classification like digit recognition, using this type of data is yet to show satisfactory results. In this paper we have tried to address these issues using different data representation and modelling techniques for capturing as much information as possible for the specific task of digit recognition, paving the way for Brain Computer Interfacing (BCI). A public dataset collected using the MUSE headband with four electrodes (TP9, AF7, AF8, TP10) has been used for this work of categorising digits (0–9). Popular deep learning methodologies like CNN (Convolutional Neural Network) model on DWT (Discrete Wavelet Transform) scalogram, CNN model on connectivity matrix (mutual information of time series against another), MLP (Multilayer Perceptron) model on extracted statistical features from EEG signals and 1D CNN on time domain EEG signals have been well experimented with in this study. Additionally, methodologies like SVC (Support Vector Classifier), Random Forest and AdaBoost on extracted features have also been showcased. Nevertheless, the study provides an insight in choosing the best suited methodology for multi-class classification of EEG signals like digit recognition for further studies.
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Das, D., Chowdhury, T., Pal, U. (2020). Analysis of Multi-class Classification of EEG Signals Using Deep Learning. In: Lu, Y., Vincent, N., Yuen, P.C., Zheng, WS., Cheriet, F., Suen, C.Y. (eds) Pattern Recognition and Artificial Intelligence. ICPRAI 2020. Lecture Notes in Computer Science(), vol 12068. Springer, Cham. https://doi.org/10.1007/978-3-030-59830-3_18
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