Electroencephalogram based communication system for locked in state person using mentally spelled tasks with optimized network model

Xu, Xinhe; Luo, Bin; Ramkumar, S.; Saravanan, S.; Balaji, M. Sundar Prakash; Dhanasekaran, S.; Thimmiaraja, J.

doi:10.1016/j.artmed.2019.101766

Cited by 11 publications

(2 citation statements)

References 23 publications

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“…205 Different methods and improvements are investigated, especially for complete LiS patients. [224][225][226][227][228][229][230][231][232][233][234][235][236][237][238][239][240][241][242] However, it is important to follow a user-centred design approach as the opinions on which mental strategies to use differed between patients and researchers. 243 Moreover, one study suggests that semantic processing can change in complete LiS patients, which could affect brain computer interface performance.…”

Section: Quality Of Life Ethical Considerations and Future Perspectivesmentioning

confidence: 99%

Locked-in syndrome revisited

Schnetzer

McCoy

Bergmann

et al. 2023

Ther Adv Neurol Disord

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The locked-in syndrome (LiS) is characterized by quadriplegia with preserved vertical eye and eyelid movements and retained cognitive abilities. Subcategorization, aetiologies and the anatomical foundation of LiS are discussed. The damage of different structures in the pons, mesencephalon and thalamus are attributed to symptoms of classical, complete and incomplete LiS and the locked-in plus syndrome, which is characterized by additional impairments of consciousness, making the clinical distinction to other chronic disorders of consciousness at times difficult. Other differential diagnoses are cognitive motor dissociation (CMD) and akinetic mutism. Treatment options are reviewed and an early, interdisciplinary and aggressive approach, including the provision of psychological support and coping strategies is favoured. The establishment of communication is a main goal of rehabilitation. Finally, the quality of life of LiS patients and ethical implications are considered. While patients with LiS report a high quality of life and well-being, medical professionals and caregivers have largely pessimistic perceptions. The negative view on life with LiS must be overthought and the autonomy and dignity of LiS patients prioritized. Knowledge has to be disseminated, diagnostics accelerated and technical support system development promoted. More well-designed research but also more awareness of the needs of LiS patients and their perception as individual persons is needed to enable a life with LiS that is worth living.

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Section: Quality Of Life Ethical Considerations and Future Perspectivesmentioning

confidence: 99%

Locked-in syndrome revisited

Schnetzer

McCoy

Bergmann

et al. 2023

Ther Adv Neurol Disord

View full text Add to dashboard Cite

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“…Jayaprbhu et al developed EEG-based BCI for ALS-affected persons using the convolution neural network and cross power spectral density for four subjects from fifteen subjects and obtained the accuracy of 91.18% and 86.88% [ 22 ]. Xiao et al modeled four-state EEG-based BCI for SCI-affected individuals using CWT featured with a hybrid neural network and obtained an accuracy of 93.86% [ 23 ]. Kai et al designed the rehabilitative device for LIS patients using local binary patterns features with Grey Wolf optimization algorithm and obtained 98.33% to 88.33% for the subject's age group between 20 and 60 from nine subjects [ 24 ].…”

Section: Background Studymentioning

confidence: 99%

Eye Movement Signal Classification for Developing Human-Computer Interface Using Electrooculogram

Thilagaraj

Ramkumar

Karthikeyan³

et al. 2021

Journal of Healthcare Engineering

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Human-computer interfaces (HCI) allow people to control electronic devices, such as computers, mouses, wheelchairs, and keyboards, by bypassing the biochannel without using motor nervous system signals. These signals permit communication between people and electronic-controllable devices. This communication is due to HCI, which facilitates lives of paralyzed patients who do not have any problems with their cognitive functioning. The major plan of this study is to test out the feasibility of nine states of HCI by using modern techniques to overcome the problem faced by the paralyzed. Analog Digital Instrument T26 with a five-electrode system was used in this method. Voluntarily twenty subjects participated in this study. The extracted signals were preprocessed by applying notch filter with a range of 50 Hz to remove the external interferences; the features were extracted by applying convolution theorem. Afterwards, extracted features were classified using Elman and distributed time delay neural network. Average classification accuracy with 90.82% and 90.56% was achieved using two network models. The accuracy of the classifier was analyzed by single-trial analysis and performances of the classifier were observed using bit transfer rate (BTR) for twenty subjects to check the feasibility of designing the HCI. The achieved results showed that the ERNN model has a greater potential to classify, identify, and recognize the EOG signal compared with distributed time delay network for most of the subjects. The control signal generated by classifiers was applied as control signals to navigate the assistive devices such as mouse, keyboard, and wheelchair activities for disabled people.

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