Limits...
EEG classification of different imaginary movements within the same limb.

Yong X, Menon C - PLoS ONE (2015)

Bottom Line: For the binary classification of imaginary grasp and elbow (goal-oriented) movements, the average accuracy achieved is 66.9%.For the 3-class problem of discriminating rest against imaginary grasp and elbow movements, the average classification accuracy achieved is 60.7%, which is greater than the random classification accuracy of 33.3%.Our results also show that goal-oriented imaginary elbow movements lead to a better classification performance compared to simple imaginary elbow movements.

View Article: PubMed Central - PubMed

Affiliation: School of Engineering Science, Simon Fraser University, Burnaby, British Columbia, Canada.

ABSTRACT
The task of discriminating the motor imagery of different movements within the same limb using electroencephalography (EEG) signals is challenging because these imaginary movements have close spatial representations on the motor cortex area. There is, however, a pressing need to succeed in this task. The reason is that the ability to classify different same-limb imaginary movements could increase the number of control dimensions of a brain-computer interface (BCI). In this paper, we propose a 3-class BCI system that discriminates EEG signals corresponding to rest, imaginary grasp movements, and imaginary elbow movements. Besides, the differences between simple motor imagery and goal-oriented motor imagery in terms of their topographical distributions and classification accuracies are also being investigated. To the best of our knowledge, both problems have not been explored in the literature. Based on the EEG data recorded from 12 able-bodied individuals, we have demonstrated that same-limb motor imagery classification is possible. For the binary classification of imaginary grasp and elbow (goal-oriented) movements, the average accuracy achieved is 66.9%. For the 3-class problem of discriminating rest against imaginary grasp and elbow movements, the average classification accuracy achieved is 60.7%, which is greater than the random classification accuracy of 33.3%. Our results also show that goal-oriented imaginary elbow movements lead to a better classification performance compared to simple imaginary elbow movements. This proposed BCI system could potentially be used in controlling a robotic rehabilitation system, which can assist stroke patients in performing task-specific exercises.

No MeSH data available.


Related in: MedlinePlus

Visual cues presented during the experiments.(a) REST: rest and relax; (b) MI-GRASP: imagine opening and closing the fingers; (c) MI-ELBOW: imagine moving the forearm up and down; (d) MI-ELBOW-GOAL: imagine reaching out for the glass of water displayed on the computer monitor and bringing it back.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4382224&req=5

pone.0121896.g002: Visual cues presented during the experiments.(a) REST: rest and relax; (b) MI-GRASP: imagine opening and closing the fingers; (c) MI-ELBOW: imagine moving the forearm up and down; (d) MI-ELBOW-GOAL: imagine reaching out for the glass of water displayed on the computer monitor and bringing it back.

Mentions: Each experiment for each participant lasted for approximately 1.5 hours. The experiment consisted of four sessions. Each session lasted 12 minutes. The participant was asked to perform different repetitive tasks according to the visual cues displayed on the computer monitor. Four different visual cues (see Fig. 2) were presented in a random order to the participant. They are listed as follows:


EEG classification of different imaginary movements within the same limb.

Yong X, Menon C - PLoS ONE (2015)

Visual cues presented during the experiments.(a) REST: rest and relax; (b) MI-GRASP: imagine opening and closing the fingers; (c) MI-ELBOW: imagine moving the forearm up and down; (d) MI-ELBOW-GOAL: imagine reaching out for the glass of water displayed on the computer monitor and bringing it back.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4382224&req=5

pone.0121896.g002: Visual cues presented during the experiments.(a) REST: rest and relax; (b) MI-GRASP: imagine opening and closing the fingers; (c) MI-ELBOW: imagine moving the forearm up and down; (d) MI-ELBOW-GOAL: imagine reaching out for the glass of water displayed on the computer monitor and bringing it back.
Mentions: Each experiment for each participant lasted for approximately 1.5 hours. The experiment consisted of four sessions. Each session lasted 12 minutes. The participant was asked to perform different repetitive tasks according to the visual cues displayed on the computer monitor. Four different visual cues (see Fig. 2) were presented in a random order to the participant. They are listed as follows:

Bottom Line: For the binary classification of imaginary grasp and elbow (goal-oriented) movements, the average accuracy achieved is 66.9%.For the 3-class problem of discriminating rest against imaginary grasp and elbow movements, the average classification accuracy achieved is 60.7%, which is greater than the random classification accuracy of 33.3%.Our results also show that goal-oriented imaginary elbow movements lead to a better classification performance compared to simple imaginary elbow movements.

View Article: PubMed Central - PubMed

Affiliation: School of Engineering Science, Simon Fraser University, Burnaby, British Columbia, Canada.

ABSTRACT
The task of discriminating the motor imagery of different movements within the same limb using electroencephalography (EEG) signals is challenging because these imaginary movements have close spatial representations on the motor cortex area. There is, however, a pressing need to succeed in this task. The reason is that the ability to classify different same-limb imaginary movements could increase the number of control dimensions of a brain-computer interface (BCI). In this paper, we propose a 3-class BCI system that discriminates EEG signals corresponding to rest, imaginary grasp movements, and imaginary elbow movements. Besides, the differences between simple motor imagery and goal-oriented motor imagery in terms of their topographical distributions and classification accuracies are also being investigated. To the best of our knowledge, both problems have not been explored in the literature. Based on the EEG data recorded from 12 able-bodied individuals, we have demonstrated that same-limb motor imagery classification is possible. For the binary classification of imaginary grasp and elbow (goal-oriented) movements, the average accuracy achieved is 66.9%. For the 3-class problem of discriminating rest against imaginary grasp and elbow movements, the average classification accuracy achieved is 60.7%, which is greater than the random classification accuracy of 33.3%. Our results also show that goal-oriented imaginary elbow movements lead to a better classification performance compared to simple imaginary elbow movements. This proposed BCI system could potentially be used in controlling a robotic rehabilitation system, which can assist stroke patients in performing task-specific exercises.

No MeSH data available.


Related in: MedlinePlus