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Single trial prediction of self-paced reaching directions from EEG signals.

Lew EY, Chavarriaga R, Silvoni S, Millán Jdel R - Front Neurosci (2014)

Bottom Line: Motivated by these results, we investigated whether slow cortical potentials (SCPs) preceding movement onset can be used to classify reaching directions and evaluated the performance using 5-fold cross-validation.For able-bodied subjects, we obtained an average decoding accuracy of 76% (chance level of 25%) at 62.5 ms before onset using the amplitude of on-going SCPs with above chance level performances between 875 to 437.5 ms prior to onset.Comparison of the decoding accuracy across different frequency ranges (i.e., SCPs, delta, theta, alpha, and gamma) yielded the best accuracy using SCPs filtered between 0.1 to 1 Hz.

View Article: PubMed Central - PubMed

Affiliation: Defitech Chair in Non-Invasive Brain-Machine Interface, Center for Neuroprosthetics, School of Engineering, Ecole Polytechnique Fédérale de Lausanne Lausanne, Switzerland ; Laboratory for Experimental Research on Behavior, Institute of Psychology, University of Lausanne Lausanne, Switzerland.

ABSTRACT
Early detection of movement intention could possibly minimize the delays in the activation of neuroprosthetic devices. As yet, single trial analysis using non-invasive approaches for understanding such movement preparation remains a challenging task. We studied the feasibility of predicting movement directions in self-paced upper limb center-out reaching tasks, i.e., spontaneous movements executed without an external cue that can better reflect natural motor behavior in humans. We reported results of non-invasive electroencephalography (EEG) recorded from mild stroke patients and able-bodied participants. Previous studies have shown that low frequency EEG oscillations are modulated by the intent to move and therefore, can be decoded prior to the movement execution. Motivated by these results, we investigated whether slow cortical potentials (SCPs) preceding movement onset can be used to classify reaching directions and evaluated the performance using 5-fold cross-validation. For able-bodied subjects, we obtained an average decoding accuracy of 76% (chance level of 25%) at 62.5 ms before onset using the amplitude of on-going SCPs with above chance level performances between 875 to 437.5 ms prior to onset. The decoding accuracy for the stroke patients was on average 47% with their paretic arms. Comparison of the decoding accuracy across different frequency ranges (i.e., SCPs, delta, theta, alpha, and gamma) yielded the best accuracy using SCPs filtered between 0.1 to 1 Hz. Across all the subjects, including stroke subjects, the best selected features were obtained mostly from the fronto-parietal regions, hence consistent with previous neurophysiological studies on arm reaching tasks. In summary, we concluded that SCPs allow the possibility of single trial decoding of reaching directions at least 312.5 ms before onset of reach.

No MeSH data available.


Related in: MedlinePlus

Decoding of movement direction based on SCPs. Stroke patients P1, P2, and P3 performed the reaching with their paretic arm. (A–D) as in Figure 2.
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Figure 4: Decoding of movement direction based on SCPs. Stroke patients P1, P2, and P3 performed the reaching with their paretic arm. (A–D) as in Figure 2.

Mentions: We evaluated our SCP-based method to decode movement direction when stroke patients performed the reaching task, notably with their paretic arm (see Figure 4). As for able-bodied subjects, we first built time-specific classifiers and then selected the best one (highest DA before movement onset) to test sensitivity and specificity. For patient P1, first panel, the channels selected from SCPs preceding onset were strongly focused at the centro-parietal regions (Figure 4A), with bilateral activation of motor areas toward the time of movement execution. DA of time-specific classifiers (Figure 4B) started to exceed chance level at 1000 ms before onset of movement. The maximum DA was 0.51 at time 250 ms before onset. Using the selected classifier during the intention period (Figure 4C), DA crossed chance level at 1475 ms before onset. However, DA decreased to random level short after and it exceeded chance level again at 550 ms and steadily increased until onset of movement. Thereafter, DA remained above chance till 500 ms after onset. This selected classifier performed at random level during the idle period (Figure 4D).


Single trial prediction of self-paced reaching directions from EEG signals.

Lew EY, Chavarriaga R, Silvoni S, Millán Jdel R - Front Neurosci (2014)

Decoding of movement direction based on SCPs. Stroke patients P1, P2, and P3 performed the reaching with their paretic arm. (A–D) as in Figure 2.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4117993&req=5

Figure 4: Decoding of movement direction based on SCPs. Stroke patients P1, P2, and P3 performed the reaching with their paretic arm. (A–D) as in Figure 2.
Mentions: We evaluated our SCP-based method to decode movement direction when stroke patients performed the reaching task, notably with their paretic arm (see Figure 4). As for able-bodied subjects, we first built time-specific classifiers and then selected the best one (highest DA before movement onset) to test sensitivity and specificity. For patient P1, first panel, the channels selected from SCPs preceding onset were strongly focused at the centro-parietal regions (Figure 4A), with bilateral activation of motor areas toward the time of movement execution. DA of time-specific classifiers (Figure 4B) started to exceed chance level at 1000 ms before onset of movement. The maximum DA was 0.51 at time 250 ms before onset. Using the selected classifier during the intention period (Figure 4C), DA crossed chance level at 1475 ms before onset. However, DA decreased to random level short after and it exceeded chance level again at 550 ms and steadily increased until onset of movement. Thereafter, DA remained above chance till 500 ms after onset. This selected classifier performed at random level during the idle period (Figure 4D).

Bottom Line: Motivated by these results, we investigated whether slow cortical potentials (SCPs) preceding movement onset can be used to classify reaching directions and evaluated the performance using 5-fold cross-validation.For able-bodied subjects, we obtained an average decoding accuracy of 76% (chance level of 25%) at 62.5 ms before onset using the amplitude of on-going SCPs with above chance level performances between 875 to 437.5 ms prior to onset.Comparison of the decoding accuracy across different frequency ranges (i.e., SCPs, delta, theta, alpha, and gamma) yielded the best accuracy using SCPs filtered between 0.1 to 1 Hz.

View Article: PubMed Central - PubMed

Affiliation: Defitech Chair in Non-Invasive Brain-Machine Interface, Center for Neuroprosthetics, School of Engineering, Ecole Polytechnique Fédérale de Lausanne Lausanne, Switzerland ; Laboratory for Experimental Research on Behavior, Institute of Psychology, University of Lausanne Lausanne, Switzerland.

ABSTRACT
Early detection of movement intention could possibly minimize the delays in the activation of neuroprosthetic devices. As yet, single trial analysis using non-invasive approaches for understanding such movement preparation remains a challenging task. We studied the feasibility of predicting movement directions in self-paced upper limb center-out reaching tasks, i.e., spontaneous movements executed without an external cue that can better reflect natural motor behavior in humans. We reported results of non-invasive electroencephalography (EEG) recorded from mild stroke patients and able-bodied participants. Previous studies have shown that low frequency EEG oscillations are modulated by the intent to move and therefore, can be decoded prior to the movement execution. Motivated by these results, we investigated whether slow cortical potentials (SCPs) preceding movement onset can be used to classify reaching directions and evaluated the performance using 5-fold cross-validation. For able-bodied subjects, we obtained an average decoding accuracy of 76% (chance level of 25%) at 62.5 ms before onset using the amplitude of on-going SCPs with above chance level performances between 875 to 437.5 ms prior to onset. The decoding accuracy for the stroke patients was on average 47% with their paretic arms. Comparison of the decoding accuracy across different frequency ranges (i.e., SCPs, delta, theta, alpha, and gamma) yielded the best accuracy using SCPs filtered between 0.1 to 1 Hz. Across all the subjects, including stroke subjects, the best selected features were obtained mostly from the fronto-parietal regions, hence consistent with previous neurophysiological studies on arm reaching tasks. In summary, we concluded that SCPs allow the possibility of single trial decoding of reaching directions at least 312.5 ms before onset of reach.

No MeSH data available.


Related in: MedlinePlus