Electroencephalography Features Extraction and Deep Patterns Analysis for Robotics Learning and Control through Brain-Computer Interface

Mattar, Ebrahim; Al-Junaid, Hessa; Al-Mutib, Khalid

doi:10.1109/3ict.2019.8910277

Cited by 2 publications

(2 citation statements)

References 12 publications

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“…A simple and less probes-based EEG system takes less computational power and results in compromised security, that is why not a potential candidate [ 15 , 16 , 17 , 18 , 19 , 20 ]. Researchers [ 21 , 22 , 23 , 24 , 25 ], propose wet EEG electrodes for high signal accuracy, calibration and less noise, interference unlike the dry leads.…”

Section: Proposed Framework Of Cognitive Authentication Systemmentioning

confidence: 99%

“…Cognitive/EEG or human brain waves are dynamic and well pattern-based mechanism with adaptive nature. In addition, the action and activities are revealed according to priorities and choices of humans towards training [ 23 ] of their mind. Most of the time real-time data sets are helping to identify the behaviors and ties in terms of his/her consistency and attitude [ 25 ].…”

Section: Proposed Framework Of Cognitive Authentication Systemmentioning

confidence: 99%

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Towards Cognitive Authentication for Smart Healthcare Applications

Sodhro

Sennersten

Ahmad

2022

Sensors

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Secure and reliable sensing plays the key role for cognitive tracking i.e., activity identification and cognitive monitoring of every individual. Over the last years there has been an increasing interest from both academia and industry in cognitive authentication also known as biometric recognition. These are an effect of individuals’ biological and physiological traits. Among various traditional biometric and physiological features, we include cognitive/brainwaves via electroencephalogram (EEG) which function as a unique performance indicator due to its reliable, flexible, and unique trait resulting in why it is hard for an un-authorized entity(ies) to breach the boundaries by stealing or mimicking them. Conventional security and privacy techniques in the medical domain are not the potential candidates to simultaneously provide both security and energy efficiency. Therefore, state-of-the art biometrics methods (i.e., machine learning, deep learning, etc.) their applications with novel solutions are investigated and recommended. The experimental setup considers EEG data analysis and interpretation of BCI. The key purpose of this setup is to reduce the number of electrodes and hence the computational power of the Random Forest (RF) classifier while testing EEG data. The performance of the random forest classifier was based on EEG datasets for 20 subjects. We found that the total number of occurred events revealed 96.1% precision in terms of chosen events.

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