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EEG-Based BCI Systems for Neurorehabilitation Applications
Published in Mridu Sahu, G. R. Sinha, Brain and Behavior Computing, 2021
Muhammad Ahmed Khan, Rig Das, John Paulin Hansen, Sadasivan Puthusserypady
According to Mao et al., “Motor imagery may be seen as a mental rehearsal of a motor act without any overt motor output” [23]. In MI-BCI, the user imagines a set of predefined mental tasks and as a result, different brain signals are generated against every assigned task. The corresponding EEG signals vary with respect to each other and these features are used to design a BCI system. The MI activities are highly noticeable in the primary motor cortex area; hence, the electrodes used for recording MI events are often placed at the C3, Cz and C4 locations. The imagined movement causes event-related synchronization (ERS) and event-related desynchronization (ERD), which can be observed as the changes in the spectral power in the µ (8–12 Hz) and ß (18–30 Hz) rhythms. Thinking of actions causes the same activation of brain regions seen when performing the actual movement; therefore, MI plays a vital role in neurorehabilitation BCIs. A detailed overview of MI-based BCIs is described in Table 8.9.
Mind the Traps! Design Guidelines for Rigorous BCI Experiments
Published in Chang S. Nam, Anton Nijholt, Fabien Lotte, Brain–Computer Interfaces Handbook, 2018
Camille Jeunet, Stefan Debener, Fabien Lotte, Jérémie Mattout, Reinhold Scherer, Catharina Zich
The variety of types of mental states that can be used within BCIs has led to classify BCIs from active over reactive up to passive (Mühl et al. 2009; Zander and Kothe 2011). As raised in Section 32.1, active BCIs require direct and conscious modulation of brain activity, whereby external stimulations serve at most as cues. Motor imagery, the mental imagination of movements, is a prominent active BCI paradigm (Pfurtscheller et al. 1997). Contrariwise, reactive BCIs rely on the indirect modulation of brain activity as a reaction to an external stimulation. Well-known examples for reactive BCIs are the P300 speller (De Vos et al. 2014; Farwell and Donchin 1988) and BCIs that are based on steady-state visual/somatosensory evoked potentials (Lalor et al. 2005; Müller-Putz et al. 2005, 2006). Finally, passive BCIs use brain activity arising without the users’ conscious modulation or without external stimulation, such as in the detection of error potentials (Zander and Kothe 2011). Additionally, different kinds of BCIs can be combined together, to make what is called a hybrid BCI (see Pfurtscheller et al. 2010 and Chapter 27 [“Hybrid Brain–Computer Interfaces and Their Applications”]). Given a BCI application, it is advisable to use the mental state that optimally balances accuracy and speed for the target application.
Examination of effectiveness of kinaesthetic haptic feedback for motor imagery-based brain-computer interface training
Published in Brain-Computer Interfaces, 2023
Isao Sakamaki, Mahdi Tavakoli, Sandra Wiebe, Kim Adams
One drawback of motor imagery paradigms is the training required to achieve sufficient accuracy to use it functionally. 14,estimated that between 15% and 30% of the non-disabled population cannot produce the ERD/ERS to control a BCI in their first session. It is recommended to perform repeated practice with feedback to acquire the skill to control the BCI system [15]. One of the most widely used BCI training protocols in the field of BCI research is the Graz training protocol [16]. Following a cued stimulus such as visual signs or symbols indicating when a user should perform motor imagery or rest, the induced sensorimotor rhythms are detected and classified according to the probability that the user is imagining movement or resting, and the user receives visual feedback on a computer screen in order to see the strength of their ERD/ERS brain response [15]. However, in order to use the BCI to control a robot in a physical play environment without a screen, we will need to examine the use of a different feedback modality.
Neural activities classification of left and right finger gestures during motor execution and motor imagery
Published in Brain-Computer Interfaces, 2021
Chao Chen, Peiji Chen, Abdelkader Nasreddine Belkacem, Lin Lu, Rui Xu, Wenjun Tan, Penghai Li, Qiang Gao, Duk Shin, Changming Wang, Dong Ming
Brain activity reflects the mental status of the brain. This activity can be recorded directly from the scalp of the brain using noninvasive measurement (e.g., electroencephalography (EEG) [1,2] and magnetoencephalography (MEG) signals [3–5]) or using invasive measurement such as electrocoticogram (ECoG) [6,7]. This brain activity has been used for designing brain–computer interface (BCI) to allow communication between the human brain and environment [8,9]. Motor imagery has been one of the most popular and widely used paradigm for developing an asynchronous BCI control [10,11]. Some motor imagery-based BCIs have been developed to help serious motor neural diseases such as Amyotrophic Lateral Sclerosis (ALS) [12,13]. Even if someone lose his/her motor ability to perform movements, he/she still can kinesthetically persist the movement imagination. Especially for some patients after brain stroke, motor imagery-based BCI is a promising approach for motor recovery, since motor imagery can enhance neural reorganization and compensation [14]. However, motor imagery-based brain–computer interface (MI-based BCI) can be also useful for elderly and healthy people [15,16]. MI-based BCI has many advantages because it doesn’t need any stimuli from outside (no need for synchronization between the user and the screen). Users can control any device (e.g., exoskeleton, wheelchair, and computer) using MI-based BCI to interact with their environment [17,18].
A screening protocol incorporating brain-computer interface feature matching considerations for augmentative and alternative communication
Published in Assistive Technology, 2020
Motor (imagery)-based BCIs use neural signals and control strategies related to imagined movements (simulation of an action without physical performance). Learning to perform motor imagery tasks has been likened to learning new physical motor actions, which is influenced by a range of factors (e.g., attention, working memory visuomotor and visuospatial skills, self-monitoring; Marinelli, Quartarone, Hallett, Frazzitta, & Ghilardi, 2017). Attention, engagement, and executive function are especially important during the early stages of motor learning for attending to and manipulating stored information (Marinelli et al., 2017; Sakai et al., 1998). For instance, during an n-back (e.g., 2-back) paradigm, individuals are asked to identify whether a presented shape in a sequence is the same as one given n turns back in the sequence, and requires individuals to monitor task performance, and update/remember information (Owen, McMillan, Laird, & Bullmore, 2005). Therefore, an n-back task was chosen for inclusion in the screening protocol to test attention, monitoring and recall (supplemental data A and B item 12A).