Design and Implementation of Steady State Visual Evoked Potential Based Brain Computer Interface Video Game

Filiz, Elifsu; Arslan, Reis Burak

doi:10.1109/melecon48756.2020.9140710

Cited by 4 publications

(3 citation statements)

References 8 publications

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“…Other examples of artistic BCI include affective states detection, playing video games and controlling virtual/augmented reality environment. Studies have demonstrated that a user can fully operate video games by SSVEP-BCI (van Vliet et al, 2012 ; Filiz and Arslan, 2020 ). Other studies have proposed how multiple users can participate in a collaborative game, in which joint decision making is required to control the gaming environment (Nijholt and Poel, 2016 ; Sekhavat, 2020 ).…”

Section: Neuroplasticity Sensors Signal Processing Modeling Anmentioning

confidence: 99%

Progress in Brain Computer Interface: Challenges and Opportunities

Saha

Mamun

Ahmed

et al. 2021

Front. Syst. Neurosci.

231

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Brain computer interfaces (BCI) provide a direct communication link between the brain and a computer or other external devices. They offer an extended degree of freedom either by strengthening or by substituting human peripheral working capacity and have potential applications in various fields such as rehabilitation, affective computing, robotics, gaming, and neuroscience. Significant research efforts on a global scale have delivered common platforms for technology standardization and help tackle highly complex and non-linear brain dynamics and related feature extraction and classification challenges. Time-variant psycho-neurophysiological fluctuations and their impact on brain signals impose another challenge for BCI researchers to transform the technology from laboratory experiments to plug-and-play daily life. This review summarizes state-of-the-art progress in the BCI field over the last decades and highlights critical challenges.

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Section: Neuroplasticity Sensors Signal Processing Modeling Anmentioning

confidence: 99%

Progress in Brain Computer Interface: Challenges and Opportunities

Saha

Mamun

Ahmed

et al. 2021

Front. Syst. Neurosci.

231

View full text Add to dashboard Cite

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“…Urządzenia oparte na wzrokowych potencjałach wywołanych, które są rejestrowane w EEG nad korą wzrokową w płacie potylicznym w odpowiedzi na bodźce wzrokowe o określonej częstotliwości, umożliwiają do pewnego stopnia sterownie urządzeniami elektronicznymi np. poprzez zarządzanie menu za pomocą skupiania wzroku na konkretnych elementach lub sterowanie w grach video za pomocą SSVEP-BCI [6]. Miały też miejsce udane próby sterowania lotem drona za pomocą BCI opartego na ERD i SSVEP [7][8][9].…”

Section: Typy Systemów Bciunclassified

Modern methods of treating neurological diseases

Bednarz

Basiaga

Trojan

et al. 2023

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Aim of the article The aim of the article is to summarize the current state of knowledge about brain-computer interfaces (BCI), i.e. systems that allow you to control electronic devices by recording the activity of nerve cells. Materials and methods The article is based on a literature review in the PubMed database. Conclusions Disorders of the nervous system pose a great therapeutic challenge due to the limited regenerative capacity of the nervous tissue. In the future, BCI may become an opportunity to improve the quality of life of patients with neurological diseases. Thanks to technological progress and increasing abilities in the field of physiology of neurons, the possibilities of using this system are increasing. Clinical trials have been performed on patients with various types of neurological deficits, including movement disorders and aphasia. Brain-computer interfaces can also help with vision loss, epilepsy, and many other conditions

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“…Reactive BCI systems, in turn, are driven by explicit communication between humans and computers by exploiting brain activity arising in reaction to external stimulation, which is indirectly modulated by the user to control an application (Wolpaw et al, 2020 ). For example, spellers using the P300 event-related potential (ERP) have been proposed and perfected over the years (e.g., Philip and George, 2020 ), as have games based on steady-state visual evoked potentials (SSVEP) (e.g., Lopez-Gordo et al, 2019 ; Filiz and Arslan, 2020 ). Active BCIs, on the other hand, derive their outputs from brain activity which is directly and consciously controlled by the user, independent of external events, to control an application.…”

Section: Introductionmentioning

confidence: 99%

Enhancing motor imagery detection efficacy using multisensory virtual reality priming

Gougeh

Falk

2023

Front. Neuroergonomics

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Brain-computer interfaces (BCI) have been developed to allow users to communicate with the external world by translating brain activity into control signals. Motor imagery (MI) has been a popular paradigm in BCI control where the user imagines movements of e.g., their left and right limbs and classifiers are then trained to detect such intent directly from electroencephalography (EEG) signals. For some users, however, it is difficult to elicit patterns in the EEG signal that can be detected with existing features and classifiers. As such, new user control strategies and training paradigms have been highly sought-after to help improve motor imagery performance. Virtual reality (VR) has emerged as one potential tool where improvements in user engagement and level of immersion have shown to improve BCI accuracy. Motor priming in VR, in turn, has shown to further enhance BCI accuracy. In this pilot study, we take the first steps to explore if multisensory VR motor priming, where haptic and olfactory stimuli are present, can improve motor imagery detection efficacy in terms of both improved accuracy and faster detection. Experiments with 10 participants equipped with a biosensor-embedded VR headset, an off-the-shelf scent diffusion device, and a haptic glove with force feedback showed that significant improvements in motor imagery detection could be achieved. Increased activity in the six common spatial pattern filters used were also observed and peak accuracy could be achieved with analysis windows that were 2 s shorter. Combined, the results suggest that multisensory motor priming prior to motor imagery could improve detection efficacy.

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Design and Implementation of Steady State Visual Evoked Potential Based Brain Computer Interface Video Game

Cited by 4 publications

References 8 publications

Progress in Brain Computer Interface: Challenges and Opportunities

Progress in Brain Computer Interface: Challenges and Opportunities

Modern methods of treating neurological diseases

Enhancing motor imagery detection efficacy using multisensory virtual reality priming

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