Facilitating communication and computer use with EEG devices for non-vocal quadriplegics

Williams, Gareth; Lee, Yee Siong; Ekanayake, Samitha W.; Pathirana, Pubudu N.; Andriske, Larissa

doi:10.1109/iciafs.2014.7069604

Cited by 6 publications

(3 citation statements)

References 9 publications

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“…After pre-processing of the raw EOG signal to remove the IFR and baseline drift, as the input features for the model, the eigenvalues related to eye fatigue are extracted. Eye rotation, according to the different rotation angles and amplitude, will produce the EOG signal with different directions and amplitude [15]. There are more than 20 features of EOG signal: Proportion of slow eye movements, the average amplitude of slow eye movements, the variance of slow eye movements, proportion of fast eye movements, peak speed of fast eye movements, average amplitude of fast eye movements, the variance of fast eye movements, blink duration, eye closure duration, eye-opening duration, eyeopening delay, eye-opening delay to blink duration ratio, blink interval, the average amplitude of blink, the peak speed of eye closure, the peak speed of eye-opening, the average speed of eye closure, eye-opening average blink amplitude, peak eye closing velocity, peak eye-opening velocity, average eye closing velocity, average eye-opening velocity, horizontal eye low frequency to high-frequency energy ratio, vertical eye low frequency to high-frequency energy ratio, etc.…”

Section: Feature Extractionmentioning

confidence: 99%

Eye Fatigue Detection through Machine Learning Based on Single Channel Electrooculography

2022

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Nowadays, eye fatigue is becoming more common globally. However, there was no objective and effective method for eye fatigue detection except the sample survey questionnaire. An eye fatigue detection method by machine learning based on the Single-Channel Electrooculography-based System is proposed. Subjects are required to finish the industry-standard questionnaires of eye fatigue; the results are used as data labels. Then, we collect their electrooculography signals through a single-channel device. From the electrooculography signals, the five most relevant feature values of eye fatigue are extracted. A machine learning model that uses the five feature values as its input is designed for eye fatigue detection. Experimental results show that there is an objective link between electrooculography and eye fatigue. This method could be used in daily eye fatigue detection and it is promised in the future.

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Section: Feature Extractionmentioning

confidence: 99%

Eye Fatigue Detection through Machine Learning Based on Single Channel Electrooculography

2022

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“…EEG is the basis for Brain Computer Interface (BCI), which extracts the user's brain signals that reflect his wishes to drive the machine. A mechanism for extracting signals from the brain was presented in [6]. The authors' objective was to design a brain-computer interface for non-vocal quadriplegics.…”

Section: Related Workmentioning

confidence: 99%

Controlling a Wheelchair Using Human-Computer Interaction

2018

IJSR

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In this paper, a prototype of an interactive wheelchair for quadriplegics and amputees is presented. Quadriplegics are people who are the victims of partial or complete paralysis of both upper and lower limbs; this is often the cause of a spinal cord injury or a disease in the area around the neck. The design presented in this paper is based on the field of Human-Computer Interaction, in which the face features of the quadriplegic are used to give him/her the opportunity to realize his/her needs. The face gestures are transformed to electrical signals using python programming language and the well-known OpenCv library for image processing. Signals are furthermore transmitted to an Arduino Uno microcontroller, which gives the orders to the wheelchair to move according to the patient's wish. The design was evaluated and the results were analysed.

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“…ese signals were used to control the machines in the lack of a normal channel. Some of the important BCIs that help paralyzed people to behave like normal people include mouse controller [9][10][11], speech synthesizer [12][13][14][15], robotic arm [16], hand controller [17], keyboard and mouse controller [18], facial expression detector [19], game controller [20], and mobile phone controller [21]. In our study, we planned to conduct a comparative study between two age groups, 20-28 and 29-40, to inspect the performance in offline and online modes using a recurrent neural network trained with a metaheuristic algorithm.…”

Section: Introductionmentioning

confidence: 99%

Classification of Electroencephalogram Signal for Developing Brain-Computer Interface Using Bioinspired Machine Learning Approach

Thilagaraj

Ramkumar

Arunkumar

et al. 2022

Computational Intelligence and Neuroscience

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Transforming human intentions into patterns to direct the devices connected externally without any body movements is called Brain-Computer Interface (BCI). It is specially designed for rehabilitation patients to overcome their disabilities. Electroencephalogram (EEG) signal is one of the famous tools to operate such devices. In this study, we planned to conduct our research with twenty subjects from different age groups from 20 to 28 and 29 to 40 using three-electrode systems to analyze the performance for developing a mobile robot for navigation using band power features and neural network architecture trained with a bioinspired algorithm. From the experiment, we recognized that the maximum classification performance was 94.66% for the young group and the minimum classification performance was 94.18% for the adult group. We conducted a recognizing accuracy test for the two contrasting age groups to interpret the individual performances. The study proved that the recognition accuracy was maximum for the young group and minimum for the adult group. Through the graphical user interface, we conducted an online test for the young and adult groups. From the online test, the same young-aged people performed highly and actively with an average accuracy of 94.00% compared with the adult people whose performance was 92.00%. From this experiment, we concluded that, due to the age factor, the signal generated by the subjects decreased slightly.

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Facilitating communication and computer use with EEG devices for non-vocal quadriplegics

Cited by 6 publications

References 9 publications

Eye Fatigue Detection through Machine Learning Based on Single Channel Electrooculography

Eye Fatigue Detection through Machine Learning Based on Single Channel Electrooculography

Controlling a Wheelchair Using Human-Computer Interaction

Classification of Electroencephalogram Signal for Developing Brain-Computer Interface Using Bioinspired Machine Learning Approach

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