Processing EEG signals acquired from a consumer grade BCI device

[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. 2010.10.1109/TNSRE.2010.204986220460212Search in Google Scholar

[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. 2013.10.1109/MRA.2012.2229936Search in Google Scholar

[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 2004.10.1109/TBME.2004.82706215188868Open DOISearch in Google Scholar

[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 2006.10.1109/TNSRE.2006.87554716792290Open DOISearch in Google Scholar

[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. 2008.10.1109/TBME.2007.89781518232384Search in Google Scholar

[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.Search in Google Scholar

[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).Search in Google Scholar

[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. 373-38210.1097/00004691-199907000-0001010478710Search in Google Scholar

[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 2000.10.1109/86.84781010896181Search in Google Scholar

[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. 1-3.10.1109/iww-BCI.2014.6782549Search in Google Scholar

[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.Search in Google Scholar

[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. 2011.10.1109/TBME.2010.207729120858575Search in Google Scholar

[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. 1102-1104.10.1109/I2MTC.2014.6860913Search in Google Scholar

[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.Search in Google Scholar

[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 2013.10.1109/TCIAIG.2012.2225623Search in Google Scholar

[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. 1-4.10.1109/ARIS.2015.7158365Search in Google Scholar

[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. 1-510.1109/ICCST.2014.7045175Search in Google Scholar

[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. 1-6.10.1109/MFI.2014.6997673Search in Google Scholar

[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/Search in Google Scholar

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

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

[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-ii1210.1136/jnnp.2005.068486Search in Google Scholar

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

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

[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]Search in Google Scholar

[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]Search in Google Scholar

[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. 260-264.10.1109/ICCCC.2018.8390468Search in Google Scholar

[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. 2014.10.1109/TAFFC.2014.2339834Search in Google Scholar

[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. 435-439.10.1109/BMEI.2008.254Search in Google Scholar

[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 201810.1007/s12652-017-0513-5Search in Google Scholar

[31] B. Hjorth, “EEG analysis based on time domain properties” in Electroencephalography and Clinical Neurophysiology, Volume 29, Issue 3, 306 – 31010.1016/0013-4694(70)90143-4Search in Google Scholar

[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 2014.10.12720/ijeee.2.2.106-110Open DOISearch in Google Scholar

[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. 2016.10.1109/TNNLS.2015.249633026595929Search in Google Scholar

[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. 1-5.10.1109/ICSPCC.2017.8242581Search in Google Scholar

[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. 352-359.10.1109/BIBM.2016.7822545Search in Google Scholar

[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. 906-909.10.1109/ICOIN.2018.8343254Search in Google Scholar

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