Development of a BCI-based gaming application to enhance cognitive control in psychiatric disorders

Dutta, Sayan; Banerjee, Tanishq; Roy, Nilanjana Dutta; Chowdhury, Bappaditya; Biswas, Arindam

doi:10.1007/s11334-020-00370-7

Cited by 8 publications

(5 citation statements)

References 13 publications

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“…One more issue of consideration is to explore the effectiveness of the developed approach for artifact removal in modern architectures of deep learning [ 15 ]. Moreover, eye-tracking and cognitive psychological attention test data could be of benefit to enhance our artifact removal approach and favor both BCI performance and interpretability [ 66 , 67 ].…”

Section: Discussionmentioning

confidence: 99%

Subject-Dependent Artifact Removal for Enhancing Motor Imagery Classifier Performance under Poor Skills

Tobón-Henao

Álvarez-Meza

Castellanos-Domínguez

2022

Sensors

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The Electroencephalography (EEG)-based motor imagery (MI) paradigm is one of the most studied technologies for Brain-Computer Interface (BCI) development. Still, the low Signal-to-Noise Ratio (SNR) poses a challenge when constructing EEG-based BCI systems. Moreover, the non-stationary and nonlinear signal issues, the low-spatial data resolution, and the inter- and intra-subject variability hamper the extraction of discriminant features. Indeed, subjects with poor motor skills have difficulties in practicing MI tasks against low SNR scenarios. Here, we propose a subject-dependent preprocessing approach that includes the well-known Surface Laplacian Filtering and Independent Component Analysis algorithms to remove signal artifacts based on the MI performance. In addition, power- and phase-based functional connectivity measures are studied to extract relevant and interpretable patterns and identify subjects of inefficency. As a result, our proposal, Subject-dependent Artifact Removal (SD-AR), improves the MI classification performance in subjects with poor motor skills. Consequently, electrooculography and volume-conduction EEG artifacts are mitigated within a functional connectivity feature-extraction strategy, which favors the classification performance of a straightforward linear classifier.

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Section: Discussionmentioning

confidence: 99%

Subject-Dependent Artifact Removal for Enhancing Motor Imagery Classifier Performance under Poor Skills

Tobón-Henao

Álvarez-Meza

Castellanos-Domínguez

2022

Sensors

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“…Compared with other consumer-grade EEG headsets, the OpenBCI “Mark IV” is cost-effective [ 56 ]. The BCIs based on the Ultracortex “Mark IV” EEG headset are mainly used for emotion recognition [ 57 – 59 ] and assistance technology [ 60 – 63 ].…”

Section: Scalp-eeg-based Bcimentioning

confidence: 99%

“…To enhance cognitive control, Dutta et al. [ 63 ] developed a BCI-based application with interactive media. Corresponding alpha and beta waves were obtained from the recorded EEG signals and regarded as parameters of attention to move the object in the proposed game.…”

Section: Scalp-eeg-based Bcimentioning

confidence: 99%

Recent Progress in Wearable Brain–Computer Interface (BCI) Devices Based on Electroencephalogram (EEG) for Medical Applications: A Review

Zhang,

Li,

Huang

et al. 2023

Health Data Sci

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Importance: Brain–computer interface (BCI) decodes and converts brain signals into machine instructions to interoperate with the external world. However, limited by the implantation risks of invasive BCIs and the operational complexity of conventional noninvasive BCIs, applications of BCIs are mainly used in laboratory or clinical environments, which are not conducive to the daily use of BCI devices. With the increasing demand for intelligent medical care, the development of wearable BCI systems is necessary. Highlights: Based on the scalp-electroencephalogram (EEG), forehead-EEG, and ear-EEG, the state-of-the-art wearable BCI devices for disease management and patient assistance are reviewed. This paper focuses on the EEG acquisition equipment of the novel wearable BCI devices and summarizes the development direction of wearable EEG-based BCI devices. Conclusions: BCI devices play an essential role in the medical field. This review briefly summarizes novel wearable EEG-based BCIs applied in the medical field and the latest progress in related technologies, emphasizing its potential to help doctors, patients, and caregivers better understand and utilize BCI devices.

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“…The neuroimaging modalities most commonly utilised for measuring brain activity in BCI applications are fNIRS [ 28 ] and electroencephalogram (EEG) [ 16 , 29 ] since these modalities are non-invasive, portable, and allow greater flexibility for participants posture (e.g., sitting upright) 180 whilst recording their brain activity. In this work, fNIRS is used to image participant’s brain activity owing to the superior spatial resolution and resistance to motion artifacts in comparison to EEG [ 30 ].…”

Section: Related Workmentioning

confidence: 99%

“…An interesting area is to directly monitor cognitive processes through Brain Computer Interfaces (BCI) to control game function [ 13 , 14 ]. Beyond controlling the game physics as in BCI, integrating gamers’ cognitive experience as a higher induction of cognition permits new kind of physiological computing [ 15 , 16 ] in video games.…”

Section: Introductionmentioning

confidence: 99%

Single-Trial Recognition of Video Gamer’s Expertise from Brain Haemodynamic and Facial Emotion Responses

et al. 2021

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With an increase in consumer demand of video gaming entertainment, the game industry is exploring novel ways of game interaction such as providing direct interfaces between the game and the gamers’ cognitive or affective responses. In this work, gamer’s brain activity has been imaged using functional near infrared spectroscopy (fNIRS) whilst they watch video of a video game (League of Legends) they play. A video of the face of the participants is also recorded for each of a total of 15 trials where a trial is defined as watching a gameplay video. From the data collected, i.e., gamer’s fNIRS data in combination with emotional state estimation from gamer’s facial expressions, the expertise level of the gamers has been decoded per trial in a multi-modal framework comprising of unsupervised deep feature learning and classification by state-of-the-art models. The best tri-class classification accuracy is obtained using a cascade of random convolutional kernel transform (ROCKET) feature extraction method and deep classifier at 91.44%. This is the first work that aims at decoding expertise level of gamers using non-restrictive and portable technologies for brain imaging, and emotional state recognition derived from gamers’ facial expressions. This work has profound implications for novel designs of future human interactions with video games and brain-controlled games.

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Development of a BCI-based gaming application to enhance cognitive control in psychiatric disorders

Cited by 8 publications

References 13 publications

Subject-Dependent Artifact Removal for Enhancing Motor Imagery Classifier Performance under Poor Skills

Subject-Dependent Artifact Removal for Enhancing Motor Imagery Classifier Performance under Poor Skills

Recent Progress in Wearable Brain–Computer Interface (BCI) Devices Based on Electroencephalogram (EEG) for Medical Applications: A Review

Single-Trial Recognition of Video Gamer’s Expertise from Brain Haemodynamic and Facial Emotion Responses

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