Feature scaling for EEG human concentration using particle swarm optimization

Electroencephalograph (EEG) is a one of recording technique that is widely used to measure human activities through brain signals. One of actively growing research in the past years is to measure human concentration using EEG. Obtaining relevant features for recognizing human concentration state becomes a challenging task due to the nature of EEG signals is a non-stationary. In the past research, various combinations of features have been employed. However, to improve the classification performance, determining the importance of each employed feature is crucially needed. In this study, feature scaling method is introduced to assign different weights for important features. Four different features are investigated in frequency domain using wavelet transform (WT). Then, particle swarm optimization (PSO) is used to scale the features while extreme learning machine (ELM) is used to classify between concentration and non-concentration states. The recorded EEG signals from Neurosky Mindwave are used to evaluate the performance of the proposed technique. The final results indicate that the proposed technique offers higher performance accuracy as compared to the methods without feature scaling.

[1]  David M. W. Powers,et al.  PSO-based dimension reduction of EEG recordings: Implications for subject transfer in BCI , 2013, Neurocomputing.

[2]  Jing Zhang,et al.  EEG Classification Approach Based on the Extreme Learning Machine and Wavelet Transform , 2012, Clinical EEG and neuroscience.

[3]  Siti Anom Ahmad,et al.  Selection of Mother Wavelet Functions for Multi-Channel EEG Signal Analysis during a Working Memory Task , 2015, Sensors.

[4]  Lavanya Thunuguntla,et al.  EEG Based Brain Controlled Robot , 2014 .

[5]  Aderemi Oluyinka Adewumi,et al.  On the Performance of Linear Decreasing Inertia Weight Particle Swarm Optimization for Global Optimization , 2013, TheScientificWorldJournal.

[6]  Ning-Han Liu,et al.  Recognizing the Degree of Human Attention Using EEG Signals from Mobile Sensors , 2013, Sensors.

[7]  Tae Jin Choi,et al.  Determination of the Concentrated State Using Multiple EEG Channels , 2014 .

[8]  Robert C. Holte,et al.  Very Simple Classification Rules Perform Well on Most Commonly Used Datasets , 1993, Machine Learning.

[9]  Isao Nambu,et al.  EEG Channel Selection Using Particle Swarm Optimization for the Classification of Auditory Event-Related Potentials , 2014, TheScientificWorldJournal.

[10]  Russell C. Eberhart,et al.  Parameter Selection in Particle Swarm Optimization , 1998, Evolutionary Programming.

[11]  Michael Tangermann,et al.  Feature selection for brain-computer interfaces , 2007 .

[12]  Larry A. Rendell,et al.  A Practical Approach to Feature Selection , 1992, ML.

[13]  Chee Kheong Siew,et al.  Extreme learning machine: Theory and applications , 2006, Neurocomputing.

[14]  Ah Chung Tsoi,et al.  Classification of EEG signals using the wavelet transform , 1997, Proceedings of 13th International Conference on Digital Signal Processing.

[15]  R. Nagarajan,et al.  Appraising human emotions using Time Frequency Analysis based EEG alpha band features , 2009, 2009 Innovative Technologies in Intelligent Systems and Industrial Applications.

[16]  Gilwon Yoon,et al.  Generation of Control Signal based on Concentration Detection using EEG signal , 2013 .

[17]  Mark A. Hall,et al.  Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning , 1999, ICML.

[18]  Yiming Yang,et al.  A Comparative Study on Feature Selection in Text Categorization , 1997, ICML.

[19]  Jian Zhang,et al.  Deep Extreme Learning Machine and Its Application in EEG Classification , 2015 .

[20]  Guang-Bin Huang,et al.  Extreme learning machine: a new learning scheme of feedforward neural networks , 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).

[21]  Huan Liu,et al.  A Probabilistic Approach to Feature Selection - A Filter Solution , 1996, ICML.