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Detecting the Intention to Move Upper Limbs from Electroencephalographic Brain Signals.

Gudiño-Mendoza B, Sanchez-Ante G, Antelis JM - Comput Math Methods Med (2016)

Bottom Line: The analysis of these EEG traces showed that significant event-related desynchronization is present before and during the execution of the movements, predominantly in the motor-related α and β frequency bands and in electrodes placed above the motor cortex.This oscillatory brain activity was used to continuously detect the intention to move the limbs, that is, to identify the motor phase prior to the actual execution of the reaching movement.The results showed, first, significant classification between relax and movement intention and, second, significant detection of movement intention prior to the onset of the executed movement.

View Article: PubMed Central - PubMed

Affiliation: Tecnologico de Monterrey, Campus Guadalajara, Avenida General Ramón Corona 2514, 45201 Zapopan, JAL, Mexico.

ABSTRACT
Early decoding of motor states directly from the brain activity is essential to develop brain-machine interfaces (BMI) for natural motor control of neuroprosthetic devices. Hence, this study aimed to investigate the detection of movement information before the actual movement occurs. This information piece could be useful to provide early control signals to drive BMI-based rehabilitation and motor assisted devices, thus providing a natural and active rehabilitation therapy. In this work, electroencephalographic (EEG) brain signals from six healthy right-handed participants were recorded during self-initiated reaching movements of the upper limbs. The analysis of these EEG traces showed that significant event-related desynchronization is present before and during the execution of the movements, predominantly in the motor-related α and β frequency bands and in electrodes placed above the motor cortex. This oscillatory brain activity was used to continuously detect the intention to move the limbs, that is, to identify the motor phase prior to the actual execution of the reaching movement. The results showed, first, significant classification between relax and movement intention and, second, significant detection of movement intention prior to the onset of the executed movement. On the basis of these results, detection of movement intention could be used in BMI settings to reduce the gap between mental motor processes and the actual movement performed by an assisted or rehabilitation robotic device.

No MeSH data available.


Time-resolved movement intention detection accuracy DA(t) (solid green line) and the empirical significant chance level of detection accuracy DAsig(t) (solid red line) of each subject. t = 0 refers to the initiation of the reaching movement. Shaded regions bounding the curves indicate the standard deviation. Vertical dotted blue lines represent the time of the maximum DA(t). Vertical dotted red lines represent the tMI.
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fig5: Time-resolved movement intention detection accuracy DA(t) (solid green line) and the empirical significant chance level of detection accuracy DAsig(t) (solid red line) of each subject. t = 0 refers to the initiation of the reaching movement. Shaded regions bounding the curves indicate the standard deviation. Vertical dotted blue lines represent the time of the maximum DA(t). Vertical dotted red lines represent the tMI.

Mentions: Figure 5 shows the time-resolved detection accuracy DA(t) and the significant chance level of the detection accuracy DAsig(t). Results are presented for each subject separately. In all subjects DA(t) is presented from t = −5 s. This is due to the following: first, the trial's initiation time tini is different across all subjects and trials and the common initiation time across all of them is t = −6 s and, second, the window size used to compute the causal features is T = 1 s. For all subjects (except number 5), DA(t) is initially at the chance level and starts to rise before the movement initiation at around t = −1 s. In other words, no movement intention is detected from −6 to ≈−1 s while detection of movement intention is observed from ≈−1 s. The maximum DA(t) is 0.92, 0.73, 0.97, 0.86, and 0.85 for subjects 1 to 6, respectively, (excluding subject 5). These peaks of detection accuracy are achieved at t = 0.7, t = 0.9, t = 0.8, and t = 0.8, for subjects 1 to 4 and for subject 6 the maximum is reached in t = 0.2 and t = 0.4 (see vertical dotted blue lines in all plots of figures). Note that DA(t) always peaks at the movement execution phase t > 0. For subject 5, DA(t) is above chance level from −6 to ≈0 s and suddenly drops at about t = 0 s. This indicates that movement intention is always detected, even before the movement intention phase t < −3 s (i.e., it is not possible to discriminate between movement intention and no movement intention) and that movement intention is at the chance level at the movement execution phase t > 0 s. Thus, no movement intention information was detected for this participant. This result agrees with the distribution and average values of classification accuracy CA presented in Figure 4 for T = 1 where subject 5 presented the lower performance.


Detecting the Intention to Move Upper Limbs from Electroencephalographic Brain Signals.

Gudiño-Mendoza B, Sanchez-Ante G, Antelis JM - Comput Math Methods Med (2016)

Time-resolved movement intention detection accuracy DA(t) (solid green line) and the empirical significant chance level of detection accuracy DAsig(t) (solid red line) of each subject. t = 0 refers to the initiation of the reaching movement. Shaded regions bounding the curves indicate the standard deviation. Vertical dotted blue lines represent the time of the maximum DA(t). Vertical dotted red lines represent the tMI.
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4863091&req=5

fig5: Time-resolved movement intention detection accuracy DA(t) (solid green line) and the empirical significant chance level of detection accuracy DAsig(t) (solid red line) of each subject. t = 0 refers to the initiation of the reaching movement. Shaded regions bounding the curves indicate the standard deviation. Vertical dotted blue lines represent the time of the maximum DA(t). Vertical dotted red lines represent the tMI.
Mentions: Figure 5 shows the time-resolved detection accuracy DA(t) and the significant chance level of the detection accuracy DAsig(t). Results are presented for each subject separately. In all subjects DA(t) is presented from t = −5 s. This is due to the following: first, the trial's initiation time tini is different across all subjects and trials and the common initiation time across all of them is t = −6 s and, second, the window size used to compute the causal features is T = 1 s. For all subjects (except number 5), DA(t) is initially at the chance level and starts to rise before the movement initiation at around t = −1 s. In other words, no movement intention is detected from −6 to ≈−1 s while detection of movement intention is observed from ≈−1 s. The maximum DA(t) is 0.92, 0.73, 0.97, 0.86, and 0.85 for subjects 1 to 6, respectively, (excluding subject 5). These peaks of detection accuracy are achieved at t = 0.7, t = 0.9, t = 0.8, and t = 0.8, for subjects 1 to 4 and for subject 6 the maximum is reached in t = 0.2 and t = 0.4 (see vertical dotted blue lines in all plots of figures). Note that DA(t) always peaks at the movement execution phase t > 0. For subject 5, DA(t) is above chance level from −6 to ≈0 s and suddenly drops at about t = 0 s. This indicates that movement intention is always detected, even before the movement intention phase t < −3 s (i.e., it is not possible to discriminate between movement intention and no movement intention) and that movement intention is at the chance level at the movement execution phase t > 0 s. Thus, no movement intention information was detected for this participant. This result agrees with the distribution and average values of classification accuracy CA presented in Figure 4 for T = 1 where subject 5 presented the lower performance.

Bottom Line: The analysis of these EEG traces showed that significant event-related desynchronization is present before and during the execution of the movements, predominantly in the motor-related α and β frequency bands and in electrodes placed above the motor cortex.This oscillatory brain activity was used to continuously detect the intention to move the limbs, that is, to identify the motor phase prior to the actual execution of the reaching movement.The results showed, first, significant classification between relax and movement intention and, second, significant detection of movement intention prior to the onset of the executed movement.

View Article: PubMed Central - PubMed

Affiliation: Tecnologico de Monterrey, Campus Guadalajara, Avenida General Ramón Corona 2514, 45201 Zapopan, JAL, Mexico.

ABSTRACT
Early decoding of motor states directly from the brain activity is essential to develop brain-machine interfaces (BMI) for natural motor control of neuroprosthetic devices. Hence, this study aimed to investigate the detection of movement information before the actual movement occurs. This information piece could be useful to provide early control signals to drive BMI-based rehabilitation and motor assisted devices, thus providing a natural and active rehabilitation therapy. In this work, electroencephalographic (EEG) brain signals from six healthy right-handed participants were recorded during self-initiated reaching movements of the upper limbs. The analysis of these EEG traces showed that significant event-related desynchronization is present before and during the execution of the movements, predominantly in the motor-related α and β frequency bands and in electrodes placed above the motor cortex. This oscillatory brain activity was used to continuously detect the intention to move the limbs, that is, to identify the motor phase prior to the actual execution of the reaching movement. The results showed, first, significant classification between relax and movement intention and, second, significant detection of movement intention prior to the onset of the executed movement. On the basis of these results, detection of movement intention could be used in BMI settings to reduce the gap between mental motor processes and the actual movement performed by an assisted or rehabilitation robotic device.

No MeSH data available.