Classification of Motor Imagery Task by Using Novel Ensemble Pruning Approach

2020 ◽  
Vol 28 (1) ◽  
pp. 85-91 ◽  
Author(s):  
Muhammad Ammar Ali ◽  
Duygu Ucuncu ◽  
Pinar Karadayi Atas ◽  
Sureyya Ozogur-Akyuz
Keyword(s):  
Motor Imagery ◽  
Imagery Task ◽  
Ensemble Pruning

Experience Influences Brain Mechanisms of Watching Dance

2011 ◽  
Vol 29 (supplement) ◽  
pp. 352-377 ◽  
Author(s):  
Seon Hee Jang ◽  
Frank E Pollick
Keyword(s):  
Motor Imagery ◽  
Visual Experience ◽  
Primary Motor Cortex ◽  
Action Observation ◽  
Brain Area ◽  
Retrosplenial Cortex ◽  
Imagery Task ◽  
Temporoparietal Junction ◽  
Action Observation Network ◽  
Viewing Experience

The study of dance has been helpful to advance our understanding of how human brain networks of action observation are influenced by experience. However previous studies have not examined the effect of extensive visual experience alone: for example, an art critic or dance fan who has a rich experience of watching dance but negligible experience performing dance. To explore the effect of pure visual experience we performed a single experiment using functional Magnetic Resonance Imaging (fMRI) to compare the neural processing of dance actions in 3 groups: a) 14 ballet dancers, b) 10 experienced viewers, c) 12 novices without any extensive dance or viewing experience. Each of the 36 participants viewed short 2-second displays of ballet derived from motion capture of a professional ballerina. These displays represented the ballerina as only points of light at the major joints. We wished to study the action observation network broadly and thus included two different types of display and two different tasks for participants to perform. The two different displays were: a) brief movies of a ballet action and b) frames from the ballet movies with the points of lights connected by lines to show a ballet posture. The two different tasks were: a) passively observe the display and b) imagine performing the action depicted in the display. The two levels of display and task were combined factorially to produce four experimental conditions (observe movie, observe posture, motor imagery of movie, motor imagery of posture). The set of stimuli used in the experiment are available for download after this paper. A random effects ANOVA was performed on brain activity and an effect of experience was obtained in seven different brain areas including: right Temporoparietal Junction (TPJ), left Retrosplenial Cortex (RSC), right Primary Somatosensory Cortex (S1), bilateral Primary Motor Cortex (M1), right Orbitofrontal Cortex (OFC), right Temporal Pole (TP). The patterns of activation were plotted in each of these areas (TPJ, RSC, S1, M1, OFC, TP) to investigate more closely how the effect of experience changed across these areas. For this analysis, novices were treated as baseline and the relative effect of experience examined in the dancer and experienced viewer groups. Interpretation of these results suggests that both visual and motor experience appear equivalent in producing more extensive early processing of dance actions in early stages of representation (TPJ and RSC) and we hypothesise that this could be due to the involvement of autobiographical memory processes. The pattern of results found for dancers in S1 and M1 suggest that their perception of dance actions are enhanced by embodied processes. For example, the S1 results are consistent with claims that this brain area shows mirror properties. The pattern of results found for the experienced viewers in OFC and TP suggests that their perception of dance actions are enhanced by cognitive processes. For example, involving aspects of social cognition and hedonic processing – the experienced viewers find the motor imagery task more pleasant and have richer connections of dance to social memory. While aspects of our interpretation are speculative the core results clearly show common and distinct aspects of how viewing experience and physical experience shape brain responses to watching dance.

Simultaneous Classification of Multiple Motor Imagery and P300 for Increase in Output Information of Brain-computer Interface

2013 ◽  
Vol 133 (3) ◽  
pp. 635-641
Author(s):  
Genzo Naito ◽  
Lui Yoshida ◽  
Takashi Numata ◽  
Yutaro Ogawa ◽  
Kiyoshi Kotani ◽  
...  
Keyword(s):  
Motor Imagery ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Output Information

Deep learning techniques for classification of electroencephalogram (EEG) motor imagery (MI) signals: a review

2021 ◽  
Author(s):  
Hamdi Altaheri ◽  
Ghulam Muhammad ◽  
Mansour Alsulaiman ◽  
Syed Umar Amin ◽  
Ghadir Ali Altuwaijri ◽  
...  
Keyword(s):  
Deep Learning ◽  
Motor Imagery ◽  
Learning Techniques ◽  
Electroencephalogram Eeg
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