Processing EEG signals acquired from a consumer grade BCI device

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Abstract

BCI (Brain-Computer Interface) is a technology which goal is to create and manage a connection between the human brain and a computer with the help of EEG signals. In the last decade consumer-grade BCI devices became available thus giving opportunity to develop BCI applications outside of clinical settings. In this paper we use a device called NeuroSky MindWave Mobile. We investigate what type of information can be deducted from the data acquired from this device, and we evaluate whether it can help us in BCI applications. Our methods of processing the data involves feature extraction methods, and neural networks. Specifically, we make experiments with finding patterns in the data by binary and multiclass classification. With these methods we could detect sharp changes in the signal such as blinking patterns, but we could not extract more complex information successfully.

[1] B. Rebsamen et al., “A Brain Controlled Wheelchair to Navigate in Familiar Environments,” in IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 18, no. 6, pp. 590-598, Dec. 2010.

[2] T. Carlson and J. del R. Millan, “Brain-Controlled Wheelchairs: A Robotic Architecture,” in IEEE Robotics and Automation Magazine, vol. 20, no. 1, pp. 65-73, March. 2013.

[3] R. Scherer, G. R. Muller, C. Neuper, B. Graimann and G. Pfurtscheller, “An asynchronously controlled EEG-based virtual keyboard: improvement of the spelling rate,” in IEEE Transactions on Biomedical Engineering, vol. 51, no. 6, pp. 979-984, June 2004.

[4] A. Jackson, C. T. Moritz, J. Mavoori, T. H. Lucas and E. E. Fetz, “The neurochip BCI: towards a neural prosthesis for upper limb function,” in IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 14, no. 2, pp. 187-190, June 2006.

[5] G. R. Muller-Putz and G. Pfurtscheller, “Control of an Electrical Prosthesis With an SSVEP-Based BCI,” in IEEE Transactions on Biomedical Engineering, vol. 55, no. 1, pp. 361-364, Jan. 2008.

[6] C. Lin et al., “Noninvasive Neural Prostheses Using Mobile and Wireless EEG,” in Proceedings of the IEEE, vol. 96, no. 7, pp. 1167-1183, July 2008.

[7] C. Guger, W. Harkam, C. Hertnaes and G. Pfurtscheller. (1999, November). “Prosthetic Control by an EEG-based Brain-Computer Interface (BCI)”. In Proc. aaate 5th european conference for the advancement of assistive technology (pp. 3-6).

[8] C. Neuper, A. Schlögl, G. Pfurtscheller (1999, July), “Enhancement of Left-Right Sensorimotor EEG Differences During Feedback-Regulated Motor Imagery”, in Journal of Clinical Neurophysiology, vol. 16, no. 4, pp. 373-382

[9] F. Babiloni et al., “Linear classification of low-resolution EEG patterns produced by imagined hand movements,” in IEEE Transactions on Rehabilitation Engineering, vol. 8, no. 2, pp. 186-188, June 2000.

[10] J. Kim, I. Kim, S. Haufe and S. Lee, “Brain-computer interface for smart vehicle: Detection of braking intention during simulated driving,” 2014 International Winter Workshop on Brain-Computer Interface (BCI), Jeongsun-kun, 2014, pp. 1-3.

[11] C-T. Lin, R-C. Wu, S-F. Liang, W-H. Chao, Y-J. Chen and T-P. Jung, “EEG-based drowsiness estimation for safety driving using independent component analysis,” in IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 52, no. 12, pp. 2726-2738, Dec. 2005.

[12] R. N. Khushaba, S. Kodagoda, S. Lal and G. Dissanayake, “Driver Drowsiness Classification Using Fuzzy Wavelet-Packet-Based Feature-Extraction Algorithm,” in IEEE Transactions on Biomedical Engineering, vol. 58, no. 1, pp. 121-131, Jan. 2011.

[13] K. George, A. Iniguez and H. Donze, “Sensing and decoding of visual stimuli using commercial Brain Computer Interface technology,” 2014 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) Proceedings, Montevideo, 2014, pp. 1102-1104.

[14] A. Kline and J. Desai, “SIMULINK®based robotic hand control using Emotiv™ EEG headset,” 2014 40th Annual Northeast Bioengineering Conference (NEBEC), Boston, MA, 2014, pp. 1-2.

[15] N. Chumerin, N. V. Manyakov, M. van Vliet, A. Robben, A. Combaz and M. M. Van Hulle, “Steady-State Visual Evoked Potential-Based Computer Gaming on a Consumer-Grade EEG Device,” in IEEE Transactions on Computational Intelligence and AI in Games, vol. 5, no. 2, pp. 100-110, June 2013.

[16] C. Lin, C. Ding, C. Liu and Y. Liu, “Development of a real-time drowsiness warning system based on an embedded system,” 2015 International Conference on Advanced Robotics and Intelligent Systems (ARIS), Taipei, 2015, pp. 1-4.

[17] C. A. Lim, Wai Chong Chia and Siew Wen Chin, “A mobile driver safety system: Analysis of single-channel EEG on drowsiness detection,” 2014 International Conference on Computational Science and Technology (ICCST), Kota Kinabalu, 2014, pp. 1-5

[18] J. He, D. Liu, Z. Wan and C. Hu, “A noninvasive real-time driving fatigue detection technology based on left prefrontal Attention and Meditation EEG,” 2014 International Conference on Multisensor Fusion and Information Integration for Intelligent Systems (MFI), Beijing, 2014, pp. 1-6.

[19] J. W. Britton, L.C. Frey, J. L. Hopp et al., authors; E.K St. Louis., L.C. Frey, editors. Electroencephalography (EEG): An Introductory Text and Atlas of Normal and Abnormal Findings in Adults, Children, and Infants [Internet]. Chicago: American Epilepsy Society; 2016. The Available from: https://www.ncbi.nlm.nih.gov/books/NBK390357/

[20] medicine.mcgill.ca, Abnormal EEG. alpha waves”, https://www.medicine.mcgill.ca/physio/vlab/biomed_signals/eeg_raw.htm [Accessed: 09-Nov-2018]

[21] E. Niedermeyer and F. H. Lopes da Silva, “Electroencephalography: Basic Principles, Clinical Applications, and Related Fields”. Lippincott Williams & Wilkins, 2005, pp. 178-180

[22] S. J. M. Smith, “EEG in neurological conditions other than epilepsy: when does it help, what does it add?” in Journal of Neurology, Neurosurgery & Psychiatry, 2005, vol. 76, pp. ii8-ii12

[23] neurosky.com, “ATTENTION eSense”, [Online]. Available: http://developer.neurosky.com/docs/doku.php?id=thinkgear_communications_protocol#attention_esense. [Accessed: 09-Nov-2018]

[24] neurosky.com, “MEDITATION eSense”, [Online]. Available: http://developer.neurosky.com/docs/doku.php?id=thinkgear_communications_protocol#meditation_esense. [Accessed: 09-Nov-2018]

[25] neurosky.com, “ASIC_EEG_POWER_INT”, [Online]. Available: http://developer.neurosky.com/docs/doku.php?id=thinkgear_communications_protocol#asic_eeg_power_int. [Accessed: 09-Nov-2018]

[26] neurosky.com, “EEG Band Power values: Units, Amplitudes, and Meaning”, [Online]. Available: http://support.neurosky.com/kb/development-2/eeg-band-power-values-units-amplitudes-and-meaning. [Accessed: 09-Nov-2018]

[27] J. Suto, S. Oniga and P. P. Sitar, “Music stimuli recognition from electroencephalogram signal with machine learning,” 2018 7th International Conference on Computers Communications and Control (ICCCC), Oradea, 2018, pp. 260-264.

[28] R. Jenke, A. Peer and M. Buss, “Feature Extraction and Selection for Emotion Recognition from EEG,” in IEEE Transactions on Affective Computing, vol. 5, no. 3, pp. 327-339, 1 July-Sept. 2014.

[29] A. Zhang, B. Yang and L. Huang, “Feature Extraction of EEG Signals Using Power Spectral Entropy,” 2008 International Conference on BioMedical Engineering and Informatics, Sanya, 2008, pp. 435-439.

[30] J Suto, S Oniga, “Efficiency investigation of artificial neural networks in human activity recognition” in Journal of Ambient Intelligence and Humanized Computing 9 (4), 1049-1060, August 2018

[31] B. Hjorth, “EEG analysis based on time domain properties” in Electroencephalography and Clinical Neurophysiology, Volume 29, Issue 3, 306 – 310

[32] S-H. Oh, Y-R. Lee, and H-N. Kim, “A Novel EEG Feature Extraction Method Using Hjorth Parameter” in International Journal of Electronics and Electrical Engineering, Vol. 2, No. 2, pp. 106-110, June 2014.

[33] Y. Liu, Y. Lin, S. Wu, C. Chuang and C. Lin, “Brain Dynamics in Predicting Driving Fatigue Using a Recurrent Self-Evolving Fuzzy Neural Network,” in IEEE Transactions on Neural Networks and Learning Systems, vol. 27, no. 2, pp. 347-360, Feb. 2016.

[34] J. Zhang, C. Yan and X. Gong, “Deep convolutional neural network for decoding motor imagery based brain computer interface,” 2017 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC), Xiamen, 2017, pp. 1-5.

[35] X. Li, D. Song, P. Zhang, G. Yu, Y. Hou and B. Hu, “Emotion recognition from multi-channel EEG data through Convolutional Recurrent Neural Network,” 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Shenzhen, 2016, pp. 352-359.

[36] H. K. Lee and Y. Choi, “A convolution neural networks scheme for classification of motor imagery EEG based on wavelet time-frequecy image,” 2018 International Conference on Information Networking (ICOIN), Chiang Mai, 2018, pp. 906-909.

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