Abstract
Measuring brain activity through Electroencephalogram (EEG) analysis for eye state prediction has attracted attention from machine learning researchers. There have been many methods for EEG analysis using supervised and unsupervised machine learning techniques. The tradeoff between the accuracy and computation time of these methods in performing the analysis is an important issue that is rarely investigated in the previous research. This paper accordingly proposes a new method for EEG signal analysis through Self-Organizing Map (SOM) clustering and Deep Belief Network (DBN) approaches to efficiently improve the computation and accuracy of the previous methods. The method is developed using SOM clustering and DBN, which is a deep layer neural network with multiple layers of Restricted Boltzmann Machines (RBMs). The results on a dataset with 14980 instances and 15 attributes representing the values of the electrodes demonstrated that the method is efficient for EEG analysis. In addition, compared with the other supervised methods, the proposed method was able to significantly improve the accuracy of the EEG prediction.
Highlights
Neda Ahmadi,1 Mehrbakhsh Nilashi,2,3 Behrouz Minaei-Bidgoli,3 Murtaza Farooque,4 Sarminah Samad,5 Nojood O
We developed our model by utilizing a hybrid of Self-Organizing Map (SOM) clustering and Deep Belief Network (DBN) techniques. e aim is to EEG human-computer interaction (HCI) brain computer interface (BCI) regularized linear regression (RLR) Logistic Regression (LR) Artificial Neural Network (ANN) SOM Particle Swarm Optimization (PSO) Radial Basis Function Neural Network (RBFNN) Discrete Wavelet Transform (DWT) Mean Square Error (MSE) Support Vector Machine (SVM) WPE Machine Learning (ML) EOG Extremely Learning Machine (ELM) Independent Component Analysis (ICA) Outlier Detection (OD) Ocular Artifacts (OA) Convolutional Neural Network (CNN) DBN Contrastive Divergence (CD) Pattern Recognition (PR) Stacked Denoising Autoencoders (SDA) ICA GA Random Forest (RF) DT NB 2D Best Matching Unit (BMU) Quantization Errors (QEs) Artificial Bee Colony (ABC) Deep SOM (DSOM) E-DSOM NWP Morlet Wavelet Neural Network (MWNN) BackPropagation Neural Network (BPNN) Restricted Boltzmann Machines (RBMs) Generative Stochastic Neural Network (GSNN) MLPNN ICs DSP Stacked Autoencoders (SAE) Belief Net (BN) DNN Hidden Markov Model (HMM) Deep Learning (DL)
We used the EEG eye state corpus which is available in the UCI machine learning repository [58]. e dataset was donated by Rosler and Suendermann [59]
Summary
Neda Ahmadi ,1 Mehrbakhsh Nilashi ,2,3 Behrouz Minaei-Bidgoli ,3 Murtaza Farooque ,4 Sarminah Samad ,5 Nojood O. Is paper proposes a new method for EEG signal analysis through Self-Organizing Map (SOM) clustering and Deep Belief Network (DBN) approaches to efficiently improve the computation and accuracy of the previous methods. Introduction ere is a field of study in human-computer interaction (HCI) [1–3], namely, brain computer interface (BCI) [4], and it has wide ranges, such as security supervision, critical safety, and applications in industry and medicine as well [5] It is quite important in the applications of medicine; for example, the disabled people are able to interact better by utilizing this way, and this is achieved by founding a method that acts like a bridge between computer and human brain. Ere are numerous scientific applications that have used Self-Organizing Map (SOM) in their work because this neural network is designed based on unsupervised learning by Kohonen [10] and it is useful for the tasks like data clustering and reduction in the high-dimensional data [11]. SOM can be investigated to recognize the existing outliers [12]. e distribution of the Quantization Errors (QEs) on the map can be helpful to detect the outlier
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