Relevant Feature Selection from a Combination of Spectral-Temporal and Spatial Features for Classification of Motor Imagery EEG

2018 ◽  
Vol 42 (5) ◽  
Author(s):  
Jyoti Singh Kirar ◽  
R. K. Agrawal
Keyword(s):  
Feature Selection ◽  
Motor Imagery ◽  
Relevant Feature ◽  
Spatial Features ◽  
Temporal And Spatial

Multiband tangent space mapping and feature selection for classification of EEG during motor imagery

2018 ◽  
Vol 15 (4) ◽  
pp. 046021 ◽  
Author(s):  
Md Rabiul Islam ◽  
Toshihisa Tanaka ◽  
Md Khademul Islam Molla
Keyword(s):  
Feature Selection ◽  
Motor Imagery ◽  
Tangent Space ◽  
Space Mapping ◽  
Selection For

Motor imagery-based EEG signals classification by combining temporal and spatial deep characteristics

2020 ◽  
Vol 13 (4) ◽  
pp. 437-453
Author(s):  
Li Xiaoling
Keyword(s):  
Motor Imagery ◽  
Classification Performance ◽  
Classification Algorithm ◽  
Eeg Signals ◽  
Spatial Characteristics ◽  
Content Type ◽  
The Arts ◽  
Temporal And Spatial ◽  
Temporal And Spatial Characteristics

PurposeIn order to improve the weak recognition accuracy and robustness of the classification algorithm for brain-computer interface (BCI), this paper proposed a novel classification algorithm for motor imagery based on temporal and spatial characteristics extracted by using convolutional neural networks (TS-CNN) model.Design/methodology/approachAccording to the proposed algorithm, a five-layer neural network model was constructed to classify the electroencephalogram (EEG) signals. Firstly, the author designed a motor imagery-based BCI experiment, and four subjects were recruited to participate in the experiment for the recording of EEG signals. Then, after the EEG signals were preprocessed, the temporal and spatial characteristics of EEG signals were extracted by longitudinal convolutional kernel and transverse convolutional kernels, respectively. Finally, the classification of motor imagery was completed by using two fully connected layers.FindingsTo validate the classification performance and efficiency of the proposed algorithm, the comparative experiments with the state-of-the-arts algorithms are applied to validate the proposed algorithm. Experimental results have shown that the proposed TS-CNN model has the best performance and efficiency in the classification of motor imagery, reflecting on the introduced accuracy, precision, recall, ROC curve and F-score indexes.Originality/valueThe proposed TS-CNN model accurately recognized the EEG signals for different tasks of motor imagery, and provided theoretical basis and technical support for the application of BCI control system in the field of rehabilitation exoskeleton.

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

scholarly journals The role of action effects in motor sequence planning and execution: exploring the influence of temporal and spatial effect anticipation

2021 ◽  
Author(s):  
Rachel M. Brown ◽  
Erik Friedgen ◽  
Iring Koch
Keyword(s):  
Action Planning ◽  
Spatial Effects ◽  
Motor Sequence ◽  
Action Sequence ◽  
Sequential Action ◽  
Spatial Features ◽  
Temporal Features ◽  
Action Sequences ◽  
Temporal And Spatial ◽  
Anticipation Effect

AbstractActions we perform every day generate perceivable outcomes with both spatial and temporal features. According to the ideomotor principle, we plan our actions by anticipating the outcomes, but this principle does not directly address how sequential movements are influenced by different outcomes. We examined how sequential action planning is influenced by the anticipation of temporal and spatial features of action outcomes. We further explored the influence of action sequence switching. Participants performed cued sequences of button presses that generated visual effects which were either spatially compatible or incompatible with the sequences, and the spatial effects appeared after a short or long delay. The sequence cues switched or repeated across trials, and the predictability of action sequence switches was varied across groups. The results showed a delay-anticipation effect for sequential action, whereby a shorter anticipated delay between action sequences and their outcomes speeded initiation and execution of the cued action sequences. Delay anticipation was increased by predictable action switching, but it was not strongly modified by the spatial compatibility of the action outcomes. The results extend previous demonstrations of delay anticipation to the context of sequential action. The temporal delay between actions and their outcomes appears to be retrieved for sequential planning and influences both the initiation and the execution of actions.

scholarly journals Semi-automated classification of colonial Microcystis by FlowCAM imaging flow cytometry in mesocosm experiment reveals high heterogeneity during seasonal bloom

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yersultan Mirasbekov ◽  
Adina Zhumakhanova ◽  
Almira Zhantuyakova ◽  
Kuanysh Sarkytbayev ◽  
Dmitry V. Malashenkov ◽  
...  
Keyword(s):  
Machine Learning ◽  
Flow Cytometry ◽  
Spatial Resolution ◽  
Mesocosm Experiment ◽  
Imaging Flow Cytometry ◽  
Leibler Divergence ◽  
Temporal And Spatial ◽  
High Level ◽  
Training Sets

AbstractA machine learning approach was employed to detect and quantify Microcystis colonial morphospecies using FlowCAM-based imaging flow cytometry. The system was trained and tested using samples from a long-term mesocosm experiment (LMWE, Central Jutland, Denmark). The statistical validation of the classification approaches was performed using Hellinger distances, Bray–Curtis dissimilarity, and Kullback–Leibler divergence. The semi-automatic classification based on well-balanced training sets from Microcystis seasonal bloom provided a high level of intergeneric accuracy (96–100%) but relatively low intrageneric accuracy (67–78%). Our results provide a proof-of-concept of how machine learning approaches can be applied to analyze the colonial microalgae. This approach allowed to evaluate Microcystis seasonal bloom in individual mesocosms with high level of temporal and spatial resolution. The observation that some Microcystis morphotypes completely disappeared and re-appeared along the mesocosm experiment timeline supports the hypothesis of the main transition pathways of colonial Microcystis morphoforms. We demonstrated that significant changes in the training sets with colonial images required for accurate classification of Microcystis spp. from time points differed by only two weeks due to Microcystis high phenotypic heterogeneity during the bloom. We conclude that automatic methods not only allow a performance level of human taxonomist, and thus be a valuable time-saving tool in the routine-like identification of colonial phytoplankton taxa, but also can be applied to increase temporal and spatial resolution of the study.

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