A Robust Low-Cost EEG Motor Imagery-Based Brain-Computer Interface

Yohanandan, Shivanthan A. C.; Kiral-Kornek, Isabell; Tang, Jianbin; Mshford, Benjamin S.; Asif, Umar; Harrer, Stefan

doi:10.1109/embc.2018.8513429

Cited by 28 publications

(13 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Nevertheless, concerns were raised about the usability of the technology, such as its association with unstable wireless communication, non-standardized sampling rate and use of gel electrodes (e.g., Chabance et al, 2019). Some researchers (e.g., Yohanandan et al, 2018) demonstrated that similar performance could be obtained with an in-house, and thus cheaper, EEG headset. A Huffington Post (New York, US) publication presented an EEG headset that was ergonomic (i.e., with dry electrodes and wireless) for <500 euros 6,7 .…”

Section: Cost and Encumberment Of Materialsmentioning

confidence: 86%

“…The use of brain-computer interfaces (BCIs) based on electroencephalography (EEG) in video games has been widely investigated. Research in adaptive training, single-trial classification (Congedo, 2013;Barachant and Congedo, 2014) and the creation of affordable EEG acquisition devices (Vos et al, 2014;Yohanandan et al, 2018) has paved the way for the development of a ubiquitous BCI technology. For example, Congedo (2013) developed "Brain Invaders, " a BCI game inspired by the famous vintage game Space Invaders (Taito, Tokyo, Japan) and based on the socalled visual P300-an electromagnetic potential produced by the brain about 300 ms after a visual stimulation.…”

Section: Introductionmentioning

confidence: 99%

“…In this respect, Kaplan et al (2013), Cattan et al (2018b), and Rashid et al (2020) provided a set of guidelines to adapt game implementation for BCI games, such as the use of turn-based games with a slow gameplay. Although Brain Invaders use a research-grade amplifier, the feasibility of using a low-cost EEG acquisition system for BCI has been demonstrated by Vos et al (2014) and Yohanandan et al (2018). These affordable headsets are comparable with research-grade amplifiers.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

The Use of Brain–Computer Interfaces in Games Is Not Ready for the General Public

Cattan¹

2021

Front. Comput. Sci.

View full text Add to dashboard Cite

Section: Cost and Encumberment Of Materialsmentioning

confidence: 86%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

The Use of Brain–Computer Interfaces in Games Is Not Ready for the General Public

Cattan¹

2021

Front. Comput. Sci.

View full text Add to dashboard Cite

“…Hand motion recognition [9][10][11][12][13][14][15][16][17], Muscle activity recognition [18][19][20][21][22][23] ECG Heartbeat signal classification , Heart disease classification [49][50][51][52][53][54][55][56][57][58][59][60][61][62][63], Sleep-stage classification [64][65][66][67][68], Emotion classification [69], age and gender prediction [70] EEG Brain functionality classification , Brain disease classification , Emotion classification [122][123][124][125][126][127][128][129], Sleep-stage classification [130][131][132][133]…”

Section: Emgmentioning

confidence: 99%

Deep Learning in Physiological Signal Data: A Survey

Rim

Sung

Min

et al. 2020

Sensors

172

View full text Add to dashboard Cite

Deep Learning (DL), a successful promising approach for discriminative and generative tasks, has recently proved its high potential in 2D medical imaging analysis; however, physiological data in the form of 1D signals have yet to be beneficially exploited from this novel approach to fulfil the desired medical tasks. Therefore, in this paper we survey the latest scientific research on deep learning in physiological signal data such as electromyogram (EMG), electrocardiogram (ECG), electroencephalogram (EEG), and electrooculogram (EOG). We found 147 papers published between January 2018 and October 2019 inclusive from various journals and publishers. The objective of this paper is to conduct a detailed study to comprehend, categorize, and compare the key parameters of the deep-learning approaches that have been used in physiological signal analysis for various medical applications. The key parameters of deep-learning approach that we review are the input data type, deep-learning task, deep-learning model, training architecture, and dataset sources. Those are the main key parameters that affect system performance. We taxonomize the research works using deep-learning method in physiological signal analysis based on: (1) physiological signal data perspective, such as data modality and medical application; and (2) deep-learning concept perspective such as training architecture and dataset sources.

show abstract

“…There have also been reported some related, EEG-NIRS works in [58][59][60][61][62][63][64], where emotion diagnoses were investigated. As these works are out of the scope of the current paper, we omit a detailed description of these papers.…”

Section: Hybrid Bcimentioning

confidence: 99%

Deep recurrent–convolutional neural network for classification of simultaneous EEG–fNIRS signals

Ghonchi

Fateh

Abolghasemi

et al. 2020

IET signal process.

View full text Add to dashboard Cite

Brain Computer Interface (BCI) is a powerful system for communicating between the brain and outside world. Traditional BCI systems work based on EEG signals only. Recently, researchers have used combination of EEG signals with other signals to improve the performance of BCI systems. Among these signals, the combination of EEG with fNIRS has achieved favorable results. In most studies, only EEGs or fNIRs have been considered as chain-like sequences, and do not consider complex correlations between adjacent signals, neither in time nor channel location. In this paper, a deep neural network model has been introduced to identify the exact objectives of the human brain by introducing temporal and spatial features. The proposed model incorporates spatial relationship between EEG and fNIRS signals. This could be implemented by transforming the sequences of these chain-like signals into hierarchical three-rank tensors. The tests show that the proposed model has a precision of 99.6%.

show abstract

A Robust Low-Cost EEG Motor Imagery-Based Brain-Computer Interface

Cited by 28 publications

References 22 publications

The Use of Brain–Computer Interfaces in Games Is Not Ready for the General Public

The Use of Brain–Computer Interfaces in Games Is Not Ready for the General Public

Deep Learning in Physiological Signal Data: A Survey

Deep recurrent–convolutional neural network for classification of simultaneous EEG–fNIRS signals

Contact Info

Product

Resources

About