Optimized Brain Computer Interface System for Unspoken Speech Recognition: Role of Wernicke Area

Authors: Nassib Abdallah, Pierre Chauvet, Abd El Salam Hajjar, Bassam Daya

Abstract:

In this paper, we propose an optimized brain computer interface (BCI) system for unspoken speech recognition, based on the fact that the constructions of unspoken words rely strongly on the Wernicke area, situated in the temporal lobe. Our BCI system has four modules: (i) the EEG Acquisition module based on a non-invasive headset with 14 electrodes; (ii) the Preprocessing module to remove noise and artifacts, using the Common Average Reference method; (iii) the Features Extraction module, using Wavelet Packet Transform (WPT); (iv) the Classification module based on a one-hidden layer artificial neural network. The present study consists of comparing the recognition accuracy of 5 Arabic words, when using all the headset electrodes or only the 4 electrodes situated near the Wernicke area, as well as the selection effect of the subbands produced by the WPT module. After applying the articial neural network on the produced database, we obtain, on the test dataset, an accuracy of 83.4% with all the electrodes and all the subbands of 8 levels of the WPT decomposition. However, by using only the 4 electrodes near Wernicke Area and the 6 middle subbands of the WPT, we obtain a high reduction of the dataset size, equal to approximately 19% of the total dataset, with 67.5% of accuracy rate. This reduction appears particularly important to improve the design of a low cost and simple to use BCI, trained for several words.

Keywords: Brain-computer interface, speech recognition, electroencephalography EEG, Wernicke area, artificial neural network.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1474789

References:

[1] Hwaiyu Geng, Jim McKeeth the brain computer interface in the internet of thingsWiley, Internet of Things and Data Analytics Handbook, December 2016.
[2] Jerry J. Shih, Dean J. Krusienski, and Jonathan R. Wolpaw. Brain-Computer Interfaces in Medicine Mayo Clin Proc. 2012 Mar; 87(3): 268279.
[3] M. Rajya Lakshmi, Dr. T. V. Prasad, Dr. V. Chandra Prakash, Survey on EEG Signal Processing MethodsInternational Journal of Advanced Research in Computer Science and Software Engineering, Volume 4, Issue 1, January 2014.
[4] Guilherme Carvalhal Ribas, The Cerebral Sulci and GyriNeurosurg Focus 56 (2): E2. PMID 20121437, 2010.
[5] Chayer C, Freedman M. Frontal lobe functionsCurr Neurol Neurosci Rep. 2001 Nov;1(6):547-52.
[6] G. H. Patel, B. J. He, M. Corbetta Attentional Networks in the Parietal Cortex Encyclopedia of Neuroscience, 2009.
[7] Priyanka A. Abhang, Bharti W. Gawali, Suresh C. Mehrotra Introduction to EEG and Speech-Based Emotion Recognition1st Edition, Elsevier, 2016.
[8] Bernard J. Baars, Nicole M. Gage The brain, Cognition, Brain, and Consciousness (Second Edition), 2010.
[9] Jan-Peter Calliess, Further Investigations on Unspoken Speech Interactive Systems Laboratorie,USA, Institut fur Theoretische Informatik, Germany, 2006.
[10] Anne Porbadnigk, EEG based Speech Recognition: Impact of Experimental Design on Performance, Fakultat fur Informatik, Universitat Karlsruhe, 2008.
[11] May Salama, Loaay ElSherif, Haytham LAshin, Tarek Gamal, Recognition of Unspoken Words Using Electrode Electroencephalographic Signals, Cognitive, The sixth International Conference on Advanced Cognitive Technologies and Applications, 2014.
[12] Nassib Abdallah, Shadi Khawandi, Bassam Daya, Pierre Chauvet, A survey of methods for the construction of a Brain Computer Interface, Sensors Networks Smart and Emerging Technologies (SENSET), 2017.