Electrooculugram based Communication System for People with Locked-in-Syndrome

Aziz, Sumair; Ibraheem, Sara; Malik, Asra; Aamir, Foha; Khan, Muhammad Umar; Shehzad, Usama

doi:10.1109/icecce49384.2020.9179323

Cited by 6 publications

(4 citation statements)

References 20 publications

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“…The functioning of the circuit has been verified by its ability to produce clear signals with minimal distortions. The amplitude values in Figures 9,10,11,12,13,14,15,16,17,18,19,20 and 21 are consistent with that of the EOG signals, thus proving that all applied electrode positions are active sites of signal acquisition that can be used as inputs in biomedical applications. Furthermore, it can be confirmed that the signals are indeed EOG signals due to the similarity in waveform shape to that of a general EOG signal [6,14].…”

Section: Discussionsupporting

confidence: 63%

“…Observing the amplitude values achieved, as shown in Figures 18,19,20 and 21, we could state that the positions N1, N2 and N4 yield the best outputs based on the strength of the signal achieved. The explanation for this result lies in the placement of the reference electrode.…”

Section: Discussionmentioning

confidence: 87%

“…Signal de-noising methods such as wavelet transform technique, amplification procedure and artifacts reduction techniques have been employed to get an excellent noise-free EOG signal [5,19]. Vertical EOG and horizontal EOG signals have been picked from the subject for various purposes [20][21][22][23]. By picking the vertical EOG signal, a low-cost driver alertness system has been designed for preventing road accidents by quickly alerting the driver if he feels drowsiness to ensure their safety [24].…”

Section: Literature Reviewmentioning

confidence: 99%

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Study of electrooculography signal acquisition sites for assistive device applications

Raj¹

2021

IJATEE

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Electrooculography (EOG) is a technique that involves the measurement of the corneo-retinal standing potential of the eye. The human eye acts as a dipole between the cornea (positive potential) and the retina (negative potential), creating an electric field around the eyeball. The resulting electric signal obtained from this field is called electrooculogram. These signals, generated by eye movements, could be measured by employing different electrode placement configurations for the acquisition. The properties of these signals change depending on the number and placement of the electrodes. The study conducted here describes EOG signal acquisition using new electrode placement configurations that employ fewer facial electrodes placed on the patient. Three pre-gelled disposable electrodes were utilized for this purpose. Only one electrode was placed on a facial location, enhancing patient comfort during the acquisition procedure. To support this study, a lowcost signal acquisition hardware was developed. Using active filtering and amplification, appropriate signal processing techniques were executed upon the horizontal EOG signal acquired to reduce noise and interference due to external conditions. Hence, this paper presents the findings of new electrode placement sites for the acquisition of EOG signals which could be used for assistive device applications while restricting the number of facial electrodes to one. Most of the studies regarding the EOG signal acquisition had been using all electrodes in the face region. In contrast, we reduced the number of electrodes in the face region, thereby providing patient comfort. The comparison was made mainly on the acquired data from these new locations to discover the configuration with the optimal signal response using data obtainedfrom seven healthy subjects. The amplitude values were being compared from the new locations with the standard acquisition sites. The findings of this study were found to have a productive result. The total gain of the system required for new electrode placement configurations was two times more than the total gain required for standard acquisition sites, and also, the amplitude was less but can be helpful for assistive device applications. The average peak to peak amplitude value of the EOG signal for the new site came around about 1.25 volt.

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Section: Discussionsupporting

confidence: 63%

Section: Discussionmentioning

confidence: 87%

Section: Literature Reviewmentioning

confidence: 99%

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Study of electrooculography signal acquisition sites for assistive device applications

Raj¹

2021

IJATEE

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“…For this reason, prognostication on neurological outcomes in patients with DoC remains a challenging task. A conspicuous number of predictive parameters in rehabilitation of patients with DoC have been reported both from the clinical world [3]- [5] and the instrumental world [6]- [8]. Multiple electroencephalography (EEG) descriptors as reactivity, alpha waves and an anteroposterior gradient (APG) have been shown to be predictive of a possible consciousness recovery [9]- [12].…”

Section: Introductionmentioning

confidence: 99%

Merging Clinical and EEG Biomarkers in an Elastic-Net Regression for Disorder of Consciousness Prognosis Prediction

Liuzzi

Grippo

Campagnini

et al. 2022

IEEE Trans. Neural Syst. Rehabil. Eng.

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Liuzzi et al. Merging clinical and EEG biomarkers in an Elastic-Net regression for disorder of consciousness prognosis prediction Abstract-Patients with Disorder of Consciousness (DoC) entering Intensive Rehabilitation Units after a severe Acquired Brain Injury have a highly variable evolution of the state of consciousness which is a complex aspect to predict. Besides clinical factors, electroencephalography has clearly shown its potential into the identification of prognostic biomarkers of consciousness recovery. In this retrospective study, with a dataset of 271 patients with DoC, we proposed three different Elastic-Net regressors trained on different datasets to predict the Coma Recovery Scale-Revised value at discharge based on data collected at admission. One dataset was completely EEG-based, one solely clinical databased and the last was composed by the union of the two. Each model was optimized, validated and tested with a robust nested cross-validation pipeline. The best models resulted in a median absolute test error of 4.54 [IQR = 4.56], 3.39 [IQR = 4.36], 3.16 [IQR = 4.13] for respectively the EEG, clinical and hybrid model. Furthermore, the hybrid model for what concerns overcoming an unresponsive wakefulness state and exiting a DoC results in an AUC of 0.91 and 0.88 respectively. Small but useful improvements are added by the EEG dataset to the clinical model for what concerns overcoming an unresponsive wakefulness state. Datadriven techniques and namely, machine learning models are hereby shown to be capable of supporting the complex decisionmaking process the practitioners must face.

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A review on the performance of brain-computer interface systems used for patients with locked-in and completely locked-in syndrome

Rezvani,

Hosseini-Zahraei,

Tootchi

et al. 2023

Cogn Neurodyn

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Electrooculugram based Communication System for People with Locked-in-Syndrome

Cited by 6 publications

References 20 publications

Study of electrooculography signal acquisition sites for assistive device applications

Study of electrooculography signal acquisition sites for assistive device applications

Merging Clinical and EEG Biomarkers in an Elastic-Net Regression for Disorder of Consciousness Prognosis Prediction

A review on the performance of brain-computer interface systems used for patients with locked-in and completely locked-in syndrome

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