EEG-based Control of a 3D Game Using 14-channel Emotiv Epoc+

Malete, Thabo N.; Moruti, Kabo; Thapelo, Tsaone Swaabow; Jamisola, Rodrigo S.

doi:10.1109/cis-ram47153.2019.9095807

Cited by 15 publications

(4 citation statements)

References 12 publications

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“…These datasets are collections of data gathered from individuals using BCI systems and provided either by research organizations, BCI manufacturers, or individual researchers. Commonly used BCI datasets include NeuroSky Mindwave [103], Emotiv EPOC+ [104,105], OpenBCI Ganglion [106], Graz University EEG Motor Imagery Database [107], PhysioNet EEG Motor Movement/Imagery Dataset [108], etc. These datasets provide a variety of recordings, including raw EEG data as well as preprocessed information such as event-related potentials (ERPs).…”

Section: Bci Datasets and Benchmarkingmentioning

confidence: 99%

State-of-the-Art on Brain-Computer Interface Technology

Pekša

Mamchur

2023

Sensors

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This paper provides a comprehensive overview of the state-of-the-art in brain–computer interfaces (BCI). It begins by providing an introduction to BCIs, describing their main operation principles and most widely used platforms. The paper then examines the various components of a BCI system, such as hardware, software, and signal processing algorithms. Finally, it looks at current trends in research related to BCI use for medical, educational, and other purposes, as well as potential future applications of this technology. The paper concludes by highlighting some key challenges that still need to be addressed before widespread adoption can occur. By presenting an up-to-date assessment of the state-of-the-art in BCI technology, this paper will provide valuable insight into where this field is heading in terms of progress and innovation.

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Section: Bci Datasets and Benchmarkingmentioning

confidence: 99%

State-of-the-Art on Brain-Computer Interface Technology

Pekša

Mamchur

2023

Sensors

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“…In [ 22 ], a P300-based BCI was designed to control a virtual wheelchair. A maze game was built in [ 23 ] with a single-channel SSVEP-based BCI, whereas in [ 24 ] the authors used MI to control forward and backward navigation in a 3D game. In addition, some practical applications have been proposed as well, for example, to have an immersive tour in a virtual library [ 25 ], controlled by a 2-task MI BCI.…”

Section: Literature Reviewmentioning

confidence: 99%

Real-Time Navigation in Google Street View® Using a Motor Imagery-Based BCI

Yang

Hulle

2023

Sensors

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Navigation in virtual worlds is ubiquitous in games and other virtual reality (VR) applications and mainly relies on external controllers. As brain–computer interfaces (BCI)s rely on mental control, bypassing traditional neural pathways, they provide to paralyzed users an alternative way to navigate. However, the majority of BCI-based navigation studies adopt cue-based visual paradigms, and the evoked brain responses are encoded into navigation commands. Although robust and accurate, these paradigms are less intuitive and comfortable for navigation compared to imagining limb movements (motor imagery, MI). However, decoding motor imagery from EEG activity is notoriously challenging. Typically, wet electrodes are used to improve EEG signal quality, including a large number of them to discriminate between movements of different limbs, and a cuedbased paradigm is used instead of a self-paced one to maximize decoding performance. Motor BCI applications primarily focus on typing applications or on navigating a wheelchair—the latter raises safety concerns—thereby calling for sensors scanning the environment for obstacles and potentially hazardous scenarios. With the help of new technologies such as virtual reality (VR), vivid graphics can be rendered, providing the user with a safe and immersive experience; and they could be used for navigation purposes, a topic that has yet to be fully explored in the BCI community. In this study, we propose a novel MI-BCI application based on an 8-dry-electrode EEG setup, with which users can explore and navigate in Google Street View®. We pay attention to system design to address the lower performance of the MI decoder due to the dry electrodes’ lower signal quality and the small number of electrodes. Specifically, we restricted the number of navigation commands by using a novel middle-level control scheme and avoided decoder mistakes by introducing eye blinks as a control signal in different navigation stages. Both offline and online experiments were conducted with 20 healthy subjects. The results showed acceptable performance, even given the limitations of the EEG set-up, which we attribute to the design of the BCI application. The study suggests the use of MI-BCI in future games and VR applications for consumers and patients temporarily or permanently devoid of muscle control.

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“…Most of the studies for EEG-controlled VR environments are motivated from the neurogaming perspective [6], and it is not always entirely clear to which extent such applications utilize brain signals and not artefacts. Here, we aim to show the feasibility of portable BCI-VR set-ups for designing experiments, aiming to answer scientific questions from the neuroscience side, as well as from the algorithm development perspective.…”

Section: Introductionmentioning

confidence: 99%

A Modular, Adaptive, Deep-Learning-Based Brain-VR Interface

Kapitonova

Haeringer

Bongk

et al. 2022

Preprint

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Brain-Computer Interfaces (BCIs) may open up new possibilities for Virtual Reality (VR) applications: BCIs may be used for active brain control of VR avatars, or to make VR content passively-adaptive based on information decoded from ongoing brain activity. Application domains for such Brain-VR Interfaces (BVRI) include medical and healthcare, entertainment, and education. Conversely, VR technology also opens up new possibilities for BCI research and development: E.g., gamified immersive BCI paradigms may improve subject engagement and long-term motivation, helping to study learning and adaptivity in the BCI-control context. Previously, we have demonstrated a first adaptive, deep-learning-based online BCI for the control of robotic assistants. Here, we describe the extension of this setup to a modular, extensible, VR-compatible online BCI setup. We describe how we integrated a classical active BCI control paradigm using motor imagery into a gamified interactive VR scenario, designed to enhance the long-term motivation of subjects. We also present an initial quality assessment of electroencephalographic (EEG) signals acquired with a dry-electrode system. We anticipate that the presented modular adaptive Brain-VR Interface will help to understand and facilitate (co-)adaptivity during long-term BCI usage.

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EEG-based Control of a 3D Game Using 14-channel Emotiv Epoc+

Cited by 15 publications

References 12 publications

State-of-the-Art on Brain-Computer Interface Technology

State-of-the-Art on Brain-Computer Interface Technology

Real-Time Navigation in Google Street View® Using a Motor Imagery-Based BCI

A Modular, Adaptive, Deep-Learning-Based Brain-VR Interface

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