Application of optimum linear filter theory to the detection of cortical signals preceding facial movement in cat

Woody, Charles D.; Nahvi, Mahmood

doi:10.1007/bf00234472

Cited by 12 publications

(5 citation statements)

References 9 publications

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“…The first goal is to estimate the temporal variations between two experimental protocols, which can be achieved using cross-correlation. Cross-correlation has been used in the past for the detection and analysis of brain signals with successful results [21,27,28]. In order to ensure the best estimations, the input to the cross correlation (for each channel) were the grand averages of the condition of interest, with the time window narrowing to the ERP elicitation.…”

Section: Estimation and Evaluation Of Latencies Among Different Proto...mentioning

confidence: 99%

“…Cross-correlation has been used in the past for the detection and analysis of brain signals with successful results [21,27,28]. In order to assure the best estimations, the input to the cross correlation (for each channel) were the grand averages of the condition of interest, with the time window narrowing to the event-related potential elicitation.…”

Section: Estimation and Evaluation Of Latencies Among Different Protomentioning

confidence: 99%

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Latency correction of event-related potentials between different experimental protocols

et al. 2014

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Abstract. Objective: A fundamental issue in EEG event-related potentials (ERPs) studies is the amount of data required to have an accurate ERP model. This also impacts the time required to train a classifier for a brain-computer interface (BCI). This issue is mainly due to the poor signal-to-noise ratio, and to the large fluctuations of the EEG caused by several sources of variability. One of these sources is directly related to the experimental protocol or application designed, and may affect to amplitude or latency variations. This usually prevents BCI classifiers to generalize among different experimental protocols. In this work, we analyze the effect of the amplitude and the latency variations among different experimental protocols based on the same type of ERP. Approach: We present a method to analyze and compensate for the latency variations in BCI applications. The algorithm has been tested on two widely used ERPs (P300 and observation error potentials), in three experimental protocols in each case. We report the ERP analysis and single-trial classification. Results and significance: The results obtained show that the designed experimental protocols significantly affect the latency of the recorded potentials but not the amplitudes; and how the use of latency-corrected data can be used to generalize the BCIs, reducing this way the calibration time when facing a new experimental protocol.

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Section: Estimation and Evaluation Of Latencies Among Different Proto...mentioning

confidence: 99%

Section: Estimation and Evaluation Of Latencies Among Different Protomentioning

confidence: 99%

Latency correction of event-related potentials between different experimental protocols

et al. 2014

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“…Since low variance is associated with high BCI accuracy, one might choose the character with minimal vCBLE; this approach yields classifier accuracies around 70%, though this is considerably lower than the online performance in the original study. Early investigations into an iterative method of building a better classifier, drawing further inspiration from [19], have met with convergence issues.…”

Section: Discussionmentioning

confidence: 99%

“…This new method can be considered a generalization of the Woody & Nahvi technique to align asynchronous responses [19]. Where Woody & Nahvi [19] used the statistical cross-correlation to find the optimal latency shift between a response and a template, our method uses a classifier score.…”

Section: Methodsmentioning

confidence: 99%

“…This new method can be considered a generalization of the Woody and Nahvi technique to align asynchronous responses [19]. Where Woody and Nahvi [19] used the statistical crosscorrelation to find the optimal latency shift between a response and a template, our method uses a classifier score. Any linear classifier, which can be expected to show significant vulnerability to latency variability, should be suitable for this technique.…”

Section: Classifier-based Latency Estimationmentioning

confidence: 99%

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Classifier-based latency estimation: a novel way to estimate and predict BCI accuracy

Thompson¹,

Warschausky²,

Huggins³

2012

J. Neural Eng.

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Objective Brain-computer interfaces (BCIs) that detect event-related potentials (ERPs) rely on classification schemes that are vulnerable to latency jitter, a phenomenon known to occur with ERPs such as the P300 response. The objective of this work was to investigate the role that latency jitter plays in BCI classification. Approach We developed a novel method, classifier-based latency estimation (CBLE), based on a generalization of Woody filtering. The technique works by presenting the time-shifted data to the classifier, and using the time shift that corresponds to the maximal classifier score. Main results The variance of CBLE estimates correlates significantly (p < 10−42) with BCI accuracy in the Farwell-Donchin BCI paradigm. Additionally, CBLE predicts same-day accuracy, even from small datasets or datasets that have already been used for classifier training, better than the accuracy on the small dataset (p < 0.05). The technique should be relatively classifier-independent, and the results were confirmed on two linear classifiers. Significance The results suggest that latency jitter may be an important cause of poor BCI performance, and methods that correct for latency jitter may improve that performance. CBLE can also be used to decrease the amount of data needed for accuracy estimation, allowing research on effects with shorter timescales.

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Electron Microscopic in Cellular and Molecular Biology

Plattner¹,

Zingsheim²

1983

Subcellular Biochemistry

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Application of optimum linear filter theory to the detection of cortical signals preceding facial movement in cat

Cited by 12 publications

References 9 publications

Latency correction of event-related potentials between different experimental protocols

Latency correction of event-related potentials between different experimental protocols

Classifier-based latency estimation: a novel way to estimate and predict BCI accuracy

Electron Microscopic in Cellular and Molecular Biology

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