Low-cost single-channel EEG based communication system for people with lock-in syndrome

Ullah, Khalil; Ali, Mohsin; Rizwan, Muhammad; Imran, Muhammad

doi:10.1109/inmic.2011.6151455

Cited by 17 publications

(8 citation statements)

References 9 publications

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“…The aim of the first BCI researchers was to create a direct communicational channel for those in need to help their quality of life, particularly of those suffering from devastating conditions, such as amyotrophic lateral sclerosis (ALS), spinal cord injury, stroke and cerebral palsy. [6][7][8][9][10][11][12][13] BCI is a direct functional interface between the brain and the computer or another device that can observe and decode the electromagnetic signals of brain activities and transfers the received information towards an external device [6,7,50]. Noninvasive BCIs have been an active research area; they include EEG, Near-infrared Spectroscopy (NIRS), functional-MRI (fMRI), among others [22][23][24].…”

Section: The Brain-computer Interfacementioning

confidence: 99%

Examining the Learning Efficiency by a Brain-Computer Interface System

2018

APH

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Brain research is one of the most significant research areas of the last decades, in which many developments and modern engineering technologies are applied. The electroencephalogram (EEG)-based brain activity observation processes are very promising and have been used in several engineering research fields. Objective: The main goal of this research was to develop a Brain-Computer Interface (BCI) system to observe the level of vigilance calculated by Think Gear-ASIC Module (TGAM1) technology and to evaluate the output with learning efficiency tests applied in cognitive neuroscience. Methods: The performance of the BCI system is evaluated in a comparative study. The BCI system was tested by thirty-two test subjects and the attention level output was compared by the Psychology Experiment Building Language's (PEBL's) Corsi block test (P CORSI) and PEBL's Ebbinghaus procedure (P EBBINGHAUS) tasks. Results: Using the BCI, we have shown statistically significant results between the BCI and the conventional cognitive neuroscience tests. The correlation between the tests and the average attention of the BCI was slightly strong for P CORSI Total Score (r=.63, p<.01 (2-tailed) and slightly strong for P EBBINGHAUS Total Correct (r=-.71, p<.01 (2-tailed). The average level of attention measured by the BCI system during the P CORSI test was 49.00%, while in case of the P EBBINGHAUS test it was 52.41% on all samples. Conclusion: The developed BCI system has a significant correlation with P CORSI and P EBBINGHAUS cognitive neuroscience tests. The BCI system is capable of observing attentional vigilance continuously. Significance: The developed BCI system is applicable to observe vigilance level in realtime while the level of attention depends on activities.

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Section: The Brain-computer Interfacementioning

confidence: 99%

Examining the Learning Efficiency by a Brain-Computer Interface System

2018

APH

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“…Figure 1 depicts an example of two trials for one subject for each data set. The presented signals are taken from C3 and C4 channel which are characterized by the appearance of rhythmic activity of the motor imagery movement [21]. …”

Section: Data Set Descriptionmentioning

confidence: 99%

“…For this range of frequencies, these data should be processed through one of the above mentioned filter techniques to keep the interesting frequency of the system in its bandwidth. In our case, the theoretical frequency response for the MI area is located on the µ-rhythm [8][9][10][11][12][13] Hz and β-rhythm [14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30] Hz [2]. In our case, the only known parameter is the attenuation band which should be close to zero decibel to avoid any distortion of the EEG information [16].…”

Section: Eeg Filter Designmentioning

confidence: 99%

An adaptive EEG filtering approach to maximize the classification accuracy in motor imagery

Belwafi¹,

Djemal²,

Ghaffari³

et al. 2014

2014 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain (CCMB)

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“…To make a Human Machine Interface (HMI) a robotic wheelchair by eye blinks or brain activity had made that can be operated by command. The command presented to the user in the screen of a PDA (Personal Digital Assistant) [14].…”

Section: Bci Approachesmentioning

confidence: 99%