On the Utility of Power Spectral Techniques With Feature Selection Techniques for Effective Mental Task Classification in Noninvasive BCI

Gupta, Akanksha; Agrawal, R. K.; Kirar, Jyoti Singh; Andreu-Pérez, Javier; Ding, Weiping; Lin, Chin‐Teng; Prasad, Mukesh

doi:10.1109/tsmc.2019.2917599

Cited by 47 publications

(22 citation statements)

References 58 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although the findings of these studies are generally consistent with the notion that brain structures are involved in perception and correction of postural perturbation, they require more research that combine the neural activities of afferent and efferent pathways. Also, certain kinds of feature selection methods may help reduce EEG channel numbers for improving device portability and processing speed [37].…”

Section: Discussionmentioning

confidence: 99%

A Pilot Study on Falling-Risk Detection Method Based on Postural Perturbation Evoked Potential Features

Jiang

Zhang

et al. 2019

Sensors

View full text Add to dashboard Cite

In the human-robot hybrid system, due to the error recognition of the pattern recognition system, the robot may perform erroneous motor execution, which may lead to falling-risk. While, the human can clearly detect the existence of errors, which is manifested in the central nervous activity characteristics. To date, the majority of studies on falling-risk detection have focused primarily on computer vision and physical signals. There are no reports of falling-risk detection methods based on neural activity. In this study, we propose a novel method to monitor multi erroneous motion events using electroencephalogram (EEG) features. There were 15 subjects who participated in this study, who kept standing with an upper limb supported posture and received an unpredictable postural perturbation. EEG signal analysis revealed a high negative peak with a maximum averaged amplitude of −14.75 ± 5.99 μV, occurring at 62 ms after postural perturbation. The xDAWN algorithm was used to reduce the high-dimension of EEG signal features. And, Bayesian linear discriminant analysis (BLDA) was used to train a classifier. The detection rate of the falling-risk onset is 98.67%. And the detection latency is 334ms, when we set detection rate beyond 90% as the standard of dangerous event onset. Further analysis showed that the falling-risk detection method based on postural perturbation evoked potential features has a good generalization ability. The model based on typical event data achieved 94.2% detection rate for unlearned atypical perturbation events. This study demonstrated the feasibility of using neural response to detect dangerous fall events.

show abstract

Section: Discussionmentioning

confidence: 99%

A Pilot Study on Falling-Risk Detection Method Based on Postural Perturbation Evoked Potential Features

Jiang

Zhang

et al. 2019

Sensors

View full text Add to dashboard Cite

show abstract

“…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%

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

et al. 2021

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…One of the commonly utilised methods to identify significant features is the Feature Selection (FS) method [16]. Nevertheless, identifying these significant features in huge dimensional data is usually a difficult task [28].…”

Section: Speeded-up Robust Featuresmentioning

confidence: 99%

Cancellable face template algorithm based on speeded-up robust features and winner-takes-all

Alwan¹,

Ku-Mahamud

2020

Multimed Tools Appl

View full text Add to dashboard Cite

Features such as face, fingerprint, and iris imprints have been used for authentication in biometric system. The toughest feature amongst these is the face. Extracting a region with the most potential face features from an image for biometric identification followed by illumination enhancement is a commonly used method. However, the region of interest extraction followed by illumination enhancement is sensitive to image face feature displacement, skewed image, and bad illumination. This research presents a cancellable face image algorithm built upon the speeded-up robust features method to extract and select features. A speeded-up robust feature approach is utilised for the image's features extraction, while Winner-Takes-All hashing is utilised for match-seeking. Finally, the features vectors are projected by utilising a random form of binary orthogonal matrice. Experiments were conducted on Yale and ORL datasets which provide grayscale images of sizes 168 × 192 and 112 × 92 pixels, respectively. The execution of the proposed algorithm was measured against several algorithms using equal error rate metric. It is found that the proposed algorithm produced an acceptable performance which indicates that this algorithm can be used in biometric security applications.

show abstract

On the Utility of Power Spectral Techniques With Feature Selection Techniques for Effective Mental Task Classification in Noninvasive BCI

Cited by 47 publications

References 58 publications

A Pilot Study on Falling-Risk Detection Method Based on Postural Perturbation Evoked Potential Features

A Pilot Study on Falling-Risk Detection Method Based on Postural Perturbation Evoked Potential Features

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

Cancellable face template algorithm based on speeded-up robust features and winner-takes-all

Contact Info

Product

Resources

About