Phase-based features for motor imagery brain-computer interfaces

Hamner, Benjamin; Leeb, Robert; Tavella, Michele; Millán, José del R.

doi:10.1109/iembs.2011.6090712

Cited by 28 publications

(27 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…However, this information has been used in classifying motor imagery-based BCI (Wang et al, 2006; Hamner et al, 2011), auditory target selection (Ng et al, 2013) and decoding continuous movement trajectories (Hammer et al, 2013). We used the instantaneous phase computed using Hilbert transform to explore the decoding power of these features.…”

Section: Resultsmentioning

confidence: 99%

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

Lew

Chavarriaga

Silvoni³

et al. 2014

Front. Neurosci.

View full text Add to dashboard Cite

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.

show abstract

Section: Resultsmentioning

confidence: 99%

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

Lew

Chavarriaga

Silvoni³

et al. 2014

Front. Neurosci.

View full text Add to dashboard Cite

show abstract

“…Recent studies of EEG-based brain-machine interface show that the classification performance of motor imagery tasks using brain connectivity features, either DTF (Billinger, Brunner, and Müller-Putz, 2013) or instantaneous phase difference (Hamner et al, 2011), is comparable to the use of customary band power features. Connectivity-based features have also been used for continuous decoding of arm trajectories from electrocorticography signals showing increased estimation accuracy with respect to spectral features (Benz et al, 2012).…”

Section: Discussionmentioning

confidence: 99%

Discriminant brain connectivity patterns of performance monitoring at average and single-trial levels

2015

View full text Add to dashboard Cite

Electrophysiological and neuroimaging evidence suggest the existence of common mechanisms for monitoring erroneous events, independent of the source of errors. Previous works have described modulations of theta activity in the medial frontal cortex elicited by either self-generated errors or erroneous feedback. In turn, similar patterns have recently been reported to appear after the observation of external errors. We report cross-regional interactions after observation of errors at both average and single-trial levels. We recorded scalp electroencephalography (EEG) signals from 15 subjects while monitoring the movement of a cursor on a computer screen. Connectivity patterns, estimated using multivariate auto-regressive models, show increased error-related modulations of the information transfer in the theta and alpha bands between frontocentral and frontolateral areas. Conversely, a decrease of connectivity in the beta band is also observed. These network patterns are similar to those elicited by self-generated errors. However, since no motor response is required, they appear to be related to intrinsic mechanisms of error processing, instead of being linked to co-activation of motor areas. Noticeably, we demonstrate that cross-regional interaction patterns can be estimated on a trial-by-trial basis. These trial-specific patterns, consistent with the multi-trial analysis, convey discriminant information on whether a trial was elicited by observation of an erroneous action. Overall, our study supports the role of frequency-specific modulations in the medial frontal cortex in coordinating cross-regional activity during cognitive monitoring at a single-trial basis.

show abstract

“…Wei et al(2007) and Zhang et al(2014) applied common average reference (CAR) before computing PLV. A discrete Laplacian spatial filter was applied by Hamner et al (2011) to measure PLV and phase difference as control signals.…”

Section: Introductionmentioning

confidence: 99%

EEG based zero-phase phase-locking value (PLV) and effects of spatial filtering during actual movement

Jian

Chen

McFarland

2017

Brain Research Bulletin

View full text Add to dashboard Cite

Phase-locking value (PLV) is a well-known feature in sensorimotor rhythm (SMR) based BCI. Zero-phase PLV has not been explored because it is generally regarded as the result of volume conduction. Because spatial filters are often used to enhance the amplitude (square root of band power (BP)) feature and attenuate volume conduction, they are frequently applied as pre-processing methods when computing PLV. However, the effects of spatial filtering on PLV are ambiguous. Therefore, this article aims to explore whether zero-phase PLV is meaningful and how this is influenced by spatial filtering. Based on archival EEG data of left and right hand movement tasks for 32 subjects, we compared BP and PLV feature using data with and without pre-processing by a large Laplacian. Results showed that using ear-referenced data, zero-phase PLV provided unique information independent of BP for task prediction which was not explained by volume conduction and was significantly decreased when a large Laplacian was applied. In other words, the large Laplacian eliminated the useful information in zero-phase PLV for task prediction suggesting that it contains effects of both amplitude and phase. Therefore, zero-phase PLV may have functional significance beyond volume conduction. The interpretation of spatial filtering may be complicated by effects of phase.

show abstract

Phase-based features for motor imagery brain-computer interfaces

Cited by 28 publications

References 9 publications

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

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

Discriminant brain connectivity patterns of performance monitoring at average and single-trial levels

EEG based zero-phase phase-locking value (PLV) and effects of spatial filtering during actual movement

Contact Info

Product

Resources

About