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Bayesian Learning for EEG Analysis
Published in Chang S. Nam, Anton Nijholt, Fabien Lotte, Brain–Computer Interfaces Handbook, 2018
By far, one of the most popularly adopted EEG activities for BCI development is event-related potential (ERP), a time- and phase-locked brain response to stimulus events of interest. Typical ERP components P300, N170, and N200 have been successfully applied to the design of BCIs. P300 is a positive deflection in EEG occurring at approximately 300 ms after a rare but task-related stimulus (i.e., oddball paradigm) (Krusienski et al. 2008), while N170 and N200 are the two negative deflections at about 170 and 200 ms, respectively. Through classifying the ERP components corresponding to controlled stimuli, an ERP-based BCI can be developed to detect the desired commands from a user. The ERP-based BCI has proven its promising potential for spelling application with a relatively robust performance for target character detection and also no requirement for subject training (Sellers and Donchin 2006; Jin et al. 2015). Another frequently adopted EEG activity for BCI development is sensorimotor rhythm (SMR), characterized as a bandpower change of particular EEG frequency band, appearing at the contralateral sensorimotor area during imagination of unilateral band movement (i.e., so-called motor imagery) (Pfurtscheller et al. 2006; Blankertz et al. 2010). Accordingly, an SMR-based BCI can be designed by recognizing the spatial pattern difference of EEG between different motor imagery tasks, typically including imagining left and right hand, foot, or tongue movements. In recent years, SMR-based BCI has shown its application value in both wheelchair control and stroke rehabilitation (Ang et al. 2011; Huang et al. 2012).
Training Therapy with BCI-based Neurofeedback Systems for Motor Rehabilitation
Published in Richard Jiang, Li Zhang, Hua-Liang Wei, Danny Crookes, Paul Chazot, Recent Advances in AI-enabled Automated Medical Diagnosis, 2022
Jingjing Luo, Qiying Cheng, Hongbo Wang, Youhao Wang, Qiang Du
Motor imagery (MI) task is one of the continuous, stable, and controllable mental activities, which is also the most widely used paradigm for induction of motor rehabilitation training. MI is an endogenous EEG signal, that is, the system must collect the subject’s own mental activity, or brain activity, without external stimuli. Quantitative calculations have shown that motor imagination and motor execution (ME) tasks can activate the same area network in the human brain, including the auxiliary motor area (SMA), the contralateral posterior central gyrus, the contralateral superior lobule, and the ipsilateral prefrontal cortex [21]. MI technology mainly extracts the characteristics of brain activity through the corresponding electroencephalography (EEG) response in the specific frequency band of the brain’s sensory motor area (SM) when a person is performing motor imagination, which is the so-called sensorimotor rhythm (SMR). It is distinct that the μ (8~12 Hz) rhythm and the β (12~30 Hz) rhythm will have a significant attenuation during MI. The phenomenon of increase or attenuation of the electrical signal before and after the appearance of the imaging task is called event-related synchronization (ERS) or event-related desynchronization (ERD). This feature makes the MI-EEG signal comparably recognizable. However, the MI-EEG signal also has some shortcomings. Some experiments have shown that a long-term motion imaging task will gradually fatigue people, thereby reducing the classification accuracy [3]. Besides, there exist inherent differences in abilities among different individuals about pure motion imagination (do not watch movement videos and rely entirely on imagination); thus the difficulty of completing rehabilitation training. Since most stroke patients are elderly people with cognitive decline, it will greatly limit their rehabilitation efficacy [56].
Initial experience with a sensorimotor rhythm-based brain-computer interface in a Parkinson’s disease patient
Published in Brain-Computer Interfaces, 2018
Kazumi Kasahara, Hideki Hoshino, Yoshihiko Furusawa, Charles Sayo DaSalla, Manabu Honda, Miho Murata, Takashi Hanakawa
The brain is not passive during the use of a brain-computer interface (BCI); it actively interacts with the BCI, tuning behavior according to feedback to achieve a desired outcome. This has led to the notion that BCIs have the potential not only to replace lost motor function, but also to restore function through neuro-rehabilitation [7]. However, some healthy subjects have initial difficulties in operating a BCI, and such difficulties could arise when BCI-based rehabilitation is used for patients with neurological disorders. Currently, many non-invasive BCIs use event-related desynchronization (ERD) of the ‘mu’ rhythm or sensorimotor rhythm (SMR) from surface electroencephalography (EEG) [8–11]. The SMR is an EEG oscillation that is mostly in the alpha range and is recorded from EEG electrodes over the central brain areas. SMR-ERD can be induced by motor execution, motor imagery, or motor observation [12]. Although machine-side factors can underlie inter-subject variability in SMR-based BCI performance, recent studies have revealed several subject-side factors that can influence BCI performance [13–15].
Hybrid Brain–Computer Interface Spellers: A Walkthrough Recent Advances in Signal Processing Methods and Challenges
Published in International Journal of Human–Computer Interaction, 2023
With the input of certain somatosensory areas, the sensorimotor rhythm (SMR) is recorded throughout the motor cortex. Event-Related Desynchronization (ERD) is defined as the decrease in SMR during which signal drifts below the relevant baseline (Rao, 2013). On the other contrary, the increase in SMR is known as Event-Related Synchronization (ERS) and the signal measured during ERS is above the specific baseline. Pfurtscheller and Neuper (2001) first proposed the synchronization and desynchronization (ERD/ERS) of sensorimotor rhythms for brain computer interface.
Towards an Online Continuous Adaptation Mechanism (OCAM) for Enhanced Engagement: An EEG Study
Published in International Journal of Human–Computer Interaction, 2019
Atef Eldenfria, Hosam Al-Samarraie
Concentration: Previous studies (e.g., Kristeva-Feige, Fritsch, Timmer, & Lücking, 2002; Marrufo, Vaquero, Cardoso, & Gomez, 2001) have shown that concentration of an individual can be estimated when the beta and SMR (sensorimotor rhythm, 12–15 Hz) waves increases, and the theta () wave decreases. Therefore, the following equation can be used to estimate the concentration index (Sung, Cho, & Um, 2012):