A high performance hybrid SSVEP based BCI speller system

Saravanakumar, D.; Reddy, M. Ramasubba

doi:10.1016/j.aei.2019.100994

Cited by 24 publications

(13 citation statements)

References 25 publications

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“…Unlike the ET, the EOG does not detect the user’s gaze but their specific eye movements, such as blinking. Saravanakumar et al proposed a one-stage hybrid speller [ 102 ] and a two-stage hybrid speller [ 103 ] based on the EOG and the SSVEP. In both studies, the EOG was used to select regions or groups while the SSVEP was used to identify targets.…”

Section: Ssvep-based Hybrid Bci Spellersmentioning

confidence: 99%

“…In both studies, the EOG was used to select regions or groups while the SSVEP was used to identify targets. In Reference [ 102 ], an SSVEP-EOG speller and an SSVEP-ET speller were compared on a one-stage paradigm and delivered similar performances. In subsequent research, visual feedback was added to the SSVEP-EOG speller, and its performance improved.…”

Section: Ssvep-based Hybrid Bci Spellersmentioning

confidence: 99%

“…Many SSVEP-based hybrid spellers have been proposed, and have delivered better performance than any single triggering method-based speller. Section 4 has provided several triggering methods that have been combined with the SSVEP to form a hybrid speller, including the visual ERP P300 [ 84 , 85 , 86 , 87 , 88 , 89 ], ET [ 96 , 97 , 98 , 99 , 100 , 101 ], EOG [ 102 , 103 ], and EMG [ 104 , 105 ].…”

Section: Trends Challenges Prospective Directions and Suggestionsmentioning

confidence: 99%

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Brain–Computer Interface Speller Based on Steady-State Visual Evoked Potential: A Review Focusing on the Stimulus Paradigm and Performance

et al. 2021

Brain Sciences

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The steady-state visual evoked potential (SSVEP), measured by the electroencephalograph (EEG), has high rates of information transfer and signal-to-noise ratio, and has been used to construct brain–computer interface (BCI) spellers. In BCI spellers, the targets of alphanumeric characters are assigned different visual stimuli and the fixation of each target generates a unique SSVEP. Matching the SSVEP to the stimulus allows users to select target letters and numbers. Many BCI spellers that harness the SSVEP have been proposed over the past two decades. Various paradigms of visual stimuli, including the procedure of target selection, layout of targets, stimulus encoding, and the combination with other triggering methods are used and considered to influence on the BCI speller performance significantly. This paper reviews these stimulus paradigms and analyzes factors influencing their performance. The fundamentals of BCI spellers are first briefly described. SSVEP-based BCI spellers, where only the SSVEP is used, are classified by stimulus paradigms and described in chronological order. Furthermore, hybrid spellers that involve the use of the SSVEP are presented in parallel. Factors influencing the performance and visual fatigue of BCI spellers are provided. Finally, prevailing challenges and prospective research directions are discussed to promote the development of BCI spellers.

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Section: Ssvep-based Hybrid Bci Spellersmentioning

confidence: 99%

Section: Ssvep-based Hybrid Bci Spellersmentioning

confidence: 99%

Section: Trends Challenges Prospective Directions and Suggestionsmentioning

confidence: 99%

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Brain–Computer Interface Speller Based on Steady-State Visual Evoked Potential: A Review Focusing on the Stimulus Paradigm and Performance

et al. 2021

Brain Sciences

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“…Most of the developed hybrid EEG-EOG systems are synchronous in nature and it does not suit well for practical usage because, the user should perform the task within a specified time window for target selection. If the task is not performed within a specified duration, it will lead to error or misclassification [26]. The users need to follow the instructions provided by the speller system and they cannot take rest while performing the task.…”

Section: Introductionmentioning

confidence: 99%

Stimulus Paradigm for an Asynchronous Concurrent SSVEP and EOG Based BCI Speller System

Duraisamy¹,

2021

IEEE Access

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The brain computer interface (BCI) speller system can be classified into synchronous and asynchronous type. In synchronous type, one of the target characters is selected with specific interval periodically even if the user is making or not making attention on the target, whereas, in asynchronous case, the target character will not be selected until a confirmation signal is received from the user. In this proposed study a novel oddball stimulus paradigm is introduced on the hybrid steady state visual evoked potential (SSVEP) and electrooculogram (EOG) speller system to achieve an asynchronous control and high performance. The proposed system consists of forty characters grouped and indexed into five flickering unique frequency visual stimuli. Each visual stimulus is assigned with eight unique characters. The characters in each group are randomly highlighted by the oddball paradigm and the user performs blink eye movement in synchrony with the desired target character highlight. An asynchronous control is achieved by EOG signal and oddball paradigm because the target character will not be selected until the user performs blink eye movement. The stimulus paradigm helps the user to select the target group and desired target character, concurrently by SSVEP and EOG signal which increases the performance of the system. The proposed asynchronous speller system is tested and validated on ten subjects. The online classification accuracy and information transfer rate (ITR) of the proposed hybrid speller system are 99.38% and 116.58 bits/min respectively. Performance metrics of the proposed system are compared with the conventional speller systems and is found to be much superior to existing systems.

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“…Various forms of electrical brain activities [4] have been used for EEG based BCI systems. Mu rhythm [5][6][7], P300 [8,9], steady state visual evoked potential [10][11][12] are some of them.…”

Section: Introductionmentioning

confidence: 99%

Multi-Sessions Outcome for EEG Feature Extraction and Classification Methods in a Motor Imagery Task

Hrișcă-Eva¹,

Lazar²

2021

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The purpose of this research is to evaluate the performances of some features extraction methods and classification algorithms for the electroencephalographic (EEG) signals recorded in a motor task imagery paradigm. The sessions were performed by the same subject in eight consecutive years. Modeling the EEG signal as an autoregressive process (by means of Itakura distance and symmetric Itakura distance), amplitude modulation (using the amplitude modulation energy index) and phase synchronization (measuring phase locking value, phase lag index and weighted phase lag index) are the methods used for getting the appropriate information. The extracted features are classified using linear discriminant analysis, quadratic discriminant analysis, Mahalanobis distance, support vector machine and k nearest neighbor classifiers. The highest classifications rates are achieved when Itakura distance with Mahalanobis distance based classifier are applied. The outcomes of this research may improve the design of assistive devices for restoration of movement and communication strength for physically disabled patients in order to rehabilitate their lost motor abilities and to improve the quality of their daily life.

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A high performance hybrid SSVEP based BCI speller system

Cited by 24 publications

References 25 publications

Brain–Computer Interface Speller Based on Steady-State Visual Evoked Potential: A Review Focusing on the Stimulus Paradigm and Performance

Brain–Computer Interface Speller Based on Steady-State Visual Evoked Potential: A Review Focusing on the Stimulus Paradigm and Performance

Stimulus Paradigm for an Asynchronous Concurrent SSVEP and EOG Based BCI Speller System

Multi-Sessions Outcome for EEG Feature Extraction and Classification Methods in a Motor Imagery Task

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