ABSTRACT
Telemedicine using information technology (IT) and communication networks is becoming common. Often, the medical doctor and the patient can discuss the problem by video teleconference and, if necessary, the patient's physiological data can be sent to the doctor. As part of this trend, we believe that brain waves can be used for telemedicine in the future. We expect that the diagnosis of remote patients will be realized by transferring electroencephalogram (EEG) data to a server or cloud. However, if EEG data are sent as they are, the data size will be significantly large. Thus, the compression of EEG data is desirable. Furthermore, should not affect the accuracy of diagnosis if data compression is performed. In this study, the relationship between the selected EEG signal features and the accuracy is investigated.
- K. Shimoda, S. Tanabe, and Y. Tobe, 2020. Investigation of relationship between eye gaze and brain waves towards smart sensing for e-learning, Sensors and Materials, Vol. 32, No. 2 (2020) 735 -- 743.Google ScholarCross Ref
- K. Sander, M. Christian, S. Mohammad, L. Jong-Seok, Y. Ashkan, E. Touradj, P. Thierry, N. Anton, and P. Ioannis, 2012. DEAP: A Database for Emotion Analysis using Physiological Signals, IEEE Trans. on Affective Computing, Vol. 3, Issue: 1 (Jan.-March 2012).Google Scholar
- L. Wei, Z. Wei-Long, and L. Bao-Liang, 2016. Emotion Recognition Using Multimodal Deep Learning, The 23rd International Conference on Neural Information Processing.Google Scholar
- Z. S. Morteza, M. Keivan, K. S. Seyed, and M. N. Ali, 2017. A Review on EEG Signals Based Emotion Recognition, International Clinical Neuroscience Journal, 4(4):118--129 (Autumn 2017).Google ScholarCross Ref
- U. R. Acharya, L. O. Shu, H. Yuki, H. T. Jen, and A. Hojjat, 2018. Automated EEG-based screening of depression using deep convolutional neural network, Computer Methods and Programs in Biomedicine, Vol. 161, Pages 103--113 (July 2018).Google Scholar
- H. Harish, V. A. David, and P. Thomas, 2019. On the role of features in human activity recognition: Proceedings of the 23rd International Symposium on Wearable Computers Pages 78--88, ISWC '19Google Scholar
Index Terms
- Scalable selection of EEG features for compression
Recommendations
Personalized attention-based EEG channel selection for epileptic seizure prediction
AbstractEpilepsy is a neurological disorder, characterized by intractable seizures with severe consequences. To predict these seizures, electroencephalogram (EEG) data has to be collected in a continuous manner. EEG signals are recorded ...
Highlights- It is possible to use only two or three EEG channels for epileptic seizure prediction.
Comparing Performance of Dry and Gel EEG Electrodes in VR using MI Paradigms
VRST '23: Proceedings of the 29th ACM Symposium on Virtual Reality Software and TechnologyBrain–computer interfaces (BCIs) are an emerging technology with numerous applications. Electroencephalogram (EEG) motor imagery (MI) is among the most common BCI paradigms and has been used extensively in healthcare applications such as post-stroke ...
Quantification of event related brain patterns for the motor imagery tasks using inter-trial variance technique
AbstractQuantification of event-related (de) synchronization (ERD/ERS) patterns is a challenging task in the field of motor imagery (MI)-based brain–computer interface (BCI). Accurately determining the optimal time and frequency band for localizing the ...
Comments