Spatial filters yield stable features for error-related potentials across conditions

Iwane, Fumiaki; Chavarriaga, Ricardo; Iturrate, Iñaki; Millán, José del R.

doi:10.1109/smc.2016.7844316

Cited by 17 publications

(16 citation statements)

References 22 publications

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“…Furthermore, the elicited ErrPs were also stable across the three recording sessions (Fig. 3), in line with previous findings (8, 18, 37). On average, 46 and 30 days elapsed between consecutive sessions, respectively (Table 2).…”

Section: Discussionsupporting

confidence: 91%

“…To build the individual decoders, ErrPs were segmented into epochs in the time window of [0.2, 0.8] s with respect to the onset of the visuomotor rotation. Firstly, to enhance the signal-to-noise ratio of the EEG, we applied a spatial filter based on canonical correlation analysis (CCA) (37, 62). This spatial filter method transforms the averaged ErrPs to a subspace containing different ERP components (63).…”

Section: Methodsmentioning

confidence: 99%

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EEG error-related potentials encode magnitude of errors and individual perceptual thresholds

Iwane

Iturrate

Chavarriaga

et al. 2022

Preprint

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Error-related potentials (ErrP) are a prominent electroencephalogram (EEG) correlate of performance monitoring, and so crucial for learning and adapting our behavior. Although there exists an agreement that ErrP signal awareness to errors, it remains poorly understood whether they encode further information. Here we report an experiment with sixteen participants during three recording sessions in which occasional visuomotor rotations of varying magnitude occurred during a cursor reaching task. We designed a brain-computer interface (BCI) to detect ErrP in single trials that provided real-time feedback to participants by changing the color of the cursor upon ErrP detection. The individual ErrP-BCI decoders exhibited good transfer across recording sessions and scalability over the varying magnitude of errors. Our results indicate that ErrPs encode not only the conscious perception of errors, but also their magnitude, in their amplitude and latency. Furthermore, a non-linear relationship between the ErrP-BCI output and the magnitude of errors predicts individual perceptual thresholds to detect rotations. The uncovered relationship is consistent with non-human primate studies, which found a similar relationship between the size of errors and simple spike activity of Purkinje cells, and we conjecture a cerebellar contribution to ErrP. Our experimental setup and findings open new avenues to probe and extend current theories of performance monitoring, which are based on response conflict tasks, by incorporating continuous human-interaction tasks as well as analysis of the ErrP complex as a whole rather than individual peaks.

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Section: Discussionsupporting

confidence: 91%

Section: Methodsmentioning

confidence: 99%

EEG error-related potentials encode magnitude of errors and individual perceptual thresholds

Iwane

Iturrate

Chavarriaga

et al. 2022

Preprint

Self Cite

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“…Although the use of spatial filters for the analysis of ERPs is not as common as for the analysis of sensorimotor rhythms, different studies have shown their advantage [49,50,63]. In particular, the methods CCA and xDAWN have been reported to yield systematic increases in classification performance of time-locked ERPs [50,63], with a slightly superiority of CCA.…”

Section: Discussionmentioning

confidence: 99%

“…The choice of this cut-off frequency was based on previous studies showing the main oscillatory signature of ErrP appears in the theta band [11,17] and the results of the time-frequency analysis. To further enhance the signal-to-noise ratio (SNR) while reducing the number of source signals, we applied a spatial filter based on canonical correlation analysis (CCA) to EEG signals within the range of (0.2, 1.0) s with respect to the onset [49,50]. The choice of the time window was determined based on the grandaveraged signals exhibiting electrophysiological difference between the correct and erroneous trials, as shown in figure 3, which is similar to the previous studies performing synchronous classification analysis [26,35,51].…”

Section: Synchronous Classificationmentioning

confidence: 99%

Invariability of EEG error-related potentials during continuous feedback protocols elicited by erroneous actions at predicted or unpredicted states

et al. 2021

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Objective. When humans perceive an erroneous action, an EEG error-related potential (ErrP) is elicited as a neural response. ErrPs have been largely investigated in discrete feedback protocols, where actions are executed at discrete steps, to enable seamless brain-computer interaction. However, there are only a few studies that investigate ErrPs in continuous feedback protocols. The objective of the present study is to better understand the differences between two types of ErrPs elicited during continuous feedback protocols, where errors may occur either at predicted or unpredicted states. We hypothesize that ErrPs of the unpredicted state is associated with longer latency as it requires higher cognitive workload to evaluate actions compared to the predicted states. Approach. Participants monitored the trajectory of an autonomous cursor that occasionally made erroneous actions on its way to the target in two conditions, namely, predicted or unpredicted states. After characterizing the ErrP waveform elicited by erroneous actions in the two conditions, we performed single-trial decoding of ErrPs in both synchronous (i.e. time-locked to the onset of the erroneous action) and asynchronous manner. Furthermore, we explored the possibility to transfer decoders built with data of one of the conditions to the other condition. Main results. As hypothesized, erroneous actions at unpredicted states gave rise to ErrPs with higher latency than erroneous actions at predicted states, a correlate of higher cognitive effort in the former condition. Moreover, ErrP decoders trained in a given condition successfully transferred to the other condition with a slight loss of classification performance. This was the case for synchronous as well as asynchronous ErrP decoding, showing the invariability of ErrPs across conditions. Significance. These results advance the characterization of ErrPs during continuous feedback protocols, enlarging the potential use of ErrPs during natural operation of brain-controlled devices as it is not necessary to have different decoders for each kind of erroneous conditions.

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“…If k number of features are extracted from each electrode, then increasing the number of electrodes (say, from 1 to m) will increase the dimension of the feature vector (dimension will increase to k m ⁎ ). Therefore to find an optimal number of electrodes that carry the most discriminative information between correct and error events spatial filtering methods such as Fishers' beamforming [22], xDAWN [23], Canonical Correlation Analysis (CCA) [24] are employed. However, the spatial filter design often involves solving an optimization problem that is generally computationally intensive.…”

Section: Introductionmentioning

confidence: 99%

Single-trial detection of EEG error-related potentials in serial visual presentation paradigm

Parashiva¹,

Vinod²

2021

Biomed. Phys. Eng. Express

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When the outcome of an event is not the same as expected, the cognitive state that monitors performance elicits a time-locked brain response termed as Error-Related Potential (ErrP). Objective – In the existing work, ErrP is not recorded when there is a disassociation between an object and its description. The objective of this work is to propose a Serial Visual Presentation (SVP) experimental paradigm to record ErrP when an image and its label are disassociated. Additionally, this work aims to propose a novel method for detecting ErrP on a single-trial basis. Method – The method followed in this work includes designing of SVP paradigm in which labeled images from six categories (bike, car, flower, fruit, cat, and dog) are presented serially. In this work, a text (visual) or an audio clip describing the image in one word is presented as the label. Further, the ErrP is detected on a single-trial basis using novel electrode-averaged features. Results - The ErrP data recorded from 11 subjects’ have consistent characteristics compared to existing ErrP literature. Detection of ErrP on a single-trial basis is carried out using a novel feature extraction method on two type labeling types separately. The best average classification accuracy achieved is 69.09±4.70% and 63.33±4.56% for the audio and visual type of labeling the image, respectively. The proposed feature extraction method achieved higher classification accuracy when compared with two existing feature extraction methods. Significance - The significance of this work is that it can be used as a Brain-Computer Interface (BCI) system for quantitative evaluation and treatment of mild cognitive impairment. This work can also find non-clinical BCI applications such as image annotation.

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Spatial filters yield stable features for error-related potentials across conditions

Cited by 17 publications

References 22 publications

EEG error-related potentials encode magnitude of errors and individual perceptual thresholds

EEG error-related potentials encode magnitude of errors and individual perceptual thresholds

Invariability of EEG error-related potentials during continuous feedback protocols elicited by erroneous actions at predicted or unpredicted states

Single-trial detection of EEG error-related potentials in serial visual presentation paradigm

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