A Prototype SSVEP Based Real Time BCI Gaming System

Martišius, Ignas; Damaševičius, Robertas

doi:10.1155/2016/3861425

Cited by 87 publications

(48 citation statements)

References 79 publications

(81 reference statements)

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“…Brain computer interface (BCI) systems can detect a user's intention and provide them with brain state-dependent feedback through certain feedback mechanisms using brain signals to assist with patient rehabilitation [22,23]. Recently, steady-state visual-evoked potential (SSVEP) technology has been introduced in BCI systems [24][25][26]. SSVEPs are natural responses to visual stimuli at specific frequencies, with the strongest response at frequencies approximately 15 Hz or 20 Hz [27].…”

Section: Introductionmentioning

confidence: 99%

Brain Computer Interface-Based Action Observation Game Enhances Mu Suppression in Patients with Stroke

et al. 2019

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Action observation (AO), based on the mirror neuron theory, is a promising strategy to promote motor cortical activation in neurorehabilitation. Brain computer interface (BCI) can detect a user’s intention and provide them with brain state-dependent feedback to assist with patient rehabilitation. We investigated the effects of a combined BCI-AO game on power of mu band attenuation in stroke patients. Nineteen patients with subacute stroke were recruited. A BCI-AO game provided real-time feedback to participants regarding their attention to a flickering action video using steady-state visual-evoked potentials. All participants watched a video of repetitive grasping actions under two conditions: (1) BCI-AO game and (2) conventional AO, in random order. In the BCI-AO game, feedback on participants’ observation scores and observation time was provided. In conventional AO, a non-flickering video and no feedback were provided. The magnitude of mu suppression in the central motor, temporal, parietal, and occipital areas was significantly higher in the BCI-AO game than in the conventional AO. The magnitude of mu suppression was significantly higher in the BCI-AO game than in the conventional AO both in the affected and unaffected hemispheres. These results support the facilitatory effects of the BCI-AO game on mu suppression over conventional AO.

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Section: Introductionmentioning

confidence: 99%

Brain Computer Interface-Based Action Observation Game Enhances Mu Suppression in Patients with Stroke

et al. 2019

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“…Martišius and Damaševičius in 2016 [55] proposed an SSVEP based BCI gaming system. The researchers developed a 3-class BCI system based on SSVEP and emotive EPOC Headset.…”

Section: Ssvep Based Virtual Gaming Applicationmentioning

confidence: 99%

SSVEP-Based BCIs

Singla¹

2018

Evolving BCI Therapy - Engaging Brain State Dynamics

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This chapter describes the method of flickering targets, eliciting fundamental frequency changes in the EEG signal of the subject, used to drive machine commands after interpretation of user's intentions. The steady-state response of the changes in the EEG caused by events such as visual stimulus applied to the subject via a computer screen is called steady-state visually evoked potential (SSVEP). This feature of the EEG signal can be used to form a basis of input to assistive devices for locked in patients to improve their quality of life, as well as for performance enhancing devices for healthy subjects. The contents of this chapter describe the SSVEP stimuli; feature extraction techniques, feature classification techniques and a few applications based on SSVEP based BCI.

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“…3). Analyzing EEG brain waves can help us understand and detect brain disorders (dementia/seizure/stroke), conduct brain imaging, analyze thoughts, control robots, and play video games using the mind (see [13,19,6,10,11,17,14,21] among others). However, EEG data sets even for normal people are rather limited in the public domain, in sharp contrast to the abundant image and speech data driving the recent advances of artificial intelligence.…”

Section: Introductionmentioning

confidence: 99%

“…The top of the line wireless headsets are Epoc and Epoc+ manufactured by Emotiv [5]. They have as many as 14 saline hydrated sensors, see [14] for an application of Epoc in a 3 class task (left, right, center) of a gaming system.…”

Section: Introductionmentioning

confidence: 99%

An Alternating Minimization Method to Train Neural Network Models for Brain Wave Classification

Sheen¹

2018

SIURO

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An alternating minimization (AM) method, which updates variables one-by-one while fixing the rest, is developed to train a neural network with low rank weights for brainwave classification. The training involves minimizing a non-smooth and nonconvex cross entropy loss function. The neural network model does a projection inside a hidden layer for low dimensional feature extraction. The sub-problem for each variable is shown to be either convex or piece-wise convex with a finite number of minima. The sub-problems are solved using the bisection method with bisection intervals analytically derived. The overall iterative AM method is descending and convergent, free of step size (learning parameter) in the standard gradient descent method. Experiments consist of noninvasive multiple electrode recordings and the classification of brain wave data. The AM method significantly outperforms the standard neural network model trained by the gradient descent method in classifying four thoughts for both normal and Alzheimer subjects.

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A Prototype SSVEP Based Real Time BCI Gaming System

Cited by 87 publications

References 79 publications

Brain Computer Interface-Based Action Observation Game Enhances Mu Suppression in Patients with Stroke

Brain Computer Interface-Based Action Observation Game Enhances Mu Suppression in Patients with Stroke

SSVEP-Based BCIs

An Alternating Minimization Method to Train Neural Network Models for Brain Wave Classification

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