Controlling the industrial robot model with the hybrid BCI based on EOG and eye tracking

Kubacki, Arkadiusz; Jakubowski, Arkadiusz

doi:10.1063/1.5066494

Cited by 9 publications

(4 citation statements)

References 13 publications

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“…Robots and vehicles are commonly used in BCI because of their great contributions to industries and rehabilitation [12], [13]. Visual stimuli could improve the performance of an ERP-based BCI system by increasing potential amplitudes.…”

Section: Introductionmentioning

confidence: 99%

The Effect of SOA on An Asynchronous ERP and VEP-Based BCI

Yang

Liu

et al. 2021

IEEE Access

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Our previous study established an asynchronous BCI system by using the oddball paradigm to simultaneously induce event-related potentials (ERPs) and visual evoked potentials (VEPs) (E-V BCIs). We found that stimulus onset asynchrony (SOA) is an important factor for performance since it significantly affects the ERP and VEP. Increasing the SOA increases the ERP, which improves the accuracy of detecting target stimuli. However, a larger SOA leads to a lower VEP frequency, which causes the VEP to have poor accuracy when discriminating between the brain states. How to balance the two potentials and accuracies is a problem. This study established eight SOAs from 100 ms to 375 ms that were composed of different interstimulus intervals and the same stimulus duration of 80 ms. We used a probability-based Fisher linear discriminant analysis (P-FLDA) classifier to calculate the classification accuracies of the ERP-based visual speller, VEP-based brain state discrimination, and E-V BCI. The results show that as the SOA increases, the amplitudes of N200 and P300 increase, and the accuracy also shows an increasing trend. However, the frequency of the VEP and the accuracies of state discrimination show downward trends. The change in accuracies of the E-V BCI system combining these two parts is nonlinear, and the SOA optimal value is 125 ms. The SOA of 125 ms yields the best accuracies of 95.83% and practical bit rate of 57.17 bits/min in the E-V BCI system, which provides a guideline for selecting the SOA to improve the performance.

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Section: Introductionmentioning

confidence: 99%

The Effect of SOA on An Asynchronous ERP and VEP-Based BCI

Yang

Liu

et al. 2021

IEEE Access

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“…Kubacki and Jakubowski [15] discussed about the Electroencephalography (EEG), Electrooculography (EOG), and Eye tracking data's theoretically rather than practically. Also presented a review of hybrid BCIs in which interface was assembled with bioactive sensors mounted on the head.…”

Section: Related Workmentioning

confidence: 99%

Accurate and Efficient Differentiation Between Normal and Epileptic Seizure of Eyes Using 13 Layer Convolution Neural Network

Brahmaiah¹,

Yarlagadda²,

Prasad³

2021

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Epileptic seizure is one which affects the normal brain activities of human being and considered to be a risky disease. The eye ball movement signals pattern plays a significant role in determining the epileptic seizure in precise manner. In addition to it, EOG signals has its influence in detecting epileptic seizure through assessment of eye ball movement signals precisely. Detecting Epilepsy using genetical based Convolutional Neural Network plays a major role in the previous research works. Conversely, the existence of background noise on eye ball signals may impact on the outcome failure. Noise aware Epileptic Seizure Detection using Thirteen Layer Convolution Neural Network (NESD-TLCNN) is adopted in this research to mitigate this issue and thereby ensuring the prediction rate more precisely. Furthermore, Hybrid Dynamic Time Wrapping based Hidden Markov Model (HDWT-HMM) is greatly utilized for primary background noise detection and removal by estimating the noise depending on distance metric. Once after the completion of noise estimation, perfect detection of epileptic seizure is accomplished using feature extraction. The peculiar features involved are saccade, fixation and blink features. Subsequently, Particle swarm optimization (PSO) technique is also involved in this research for optimal feature selection. Thirteen Layer Convolution Neural Network (TLCNN) is applied at last for learning and differentiation of epileptic seizure from the normal eyes. This research is being carried out in MATLAB platform which also reveals that the anticipated methodology produces improved outcomes when contrasted with the existing research work.

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“…Majority of the BCI platforms use single EEG paradigm, which may not work for all users and could also generate false recognition. In the recent years, BCI systems have been enhanced by using multiple paradigms to improve accuracy and speed to control external applications [8] , [9] , [10] , [11] , [12] . Possibility of detecting both P300 component and SSVEP activity simultaneously has been confirmed by previous studies [13] .…”

Section: Hardware In Contextmentioning

confidence: 99%