Error-related electrocorticographic activity in humans during continuous movements

Milekovic, Tomislav; Ball, Tonio; Schulze‐Bonhage, Andreas; Aertsen, Ad; Mehring, Carsten

doi:10.1088/1741-2560/9/2/026007

Cited by 52 publications

(76 citation statements)

References 62 publications

(70 reference statements)

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“…Importantly, error-related signals are not only present in EEG, but several works have shown their existence also in semiinvasive [19] and invasive recording methods [20]. As future work, it is still pending to test the proposed results with a larger pool of subjects, and the feasibility of decoding this signal with closed-loop experiments.…”

Section: Discussionmentioning

confidence: 95%

Asynchronous Decoding of Error Potentials during the Monitoring of a Reaching Task

Omedes

Iturrate

Chavarriaga

et al. 2015

2015 IEEE International Conference on Systems, Man, and Cybernetics

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Abstract-Brain-machine interfaces (BMIs) have demonstrated how they can be used for reaching tasks with both invasive and non-invasive signal recording methods. Despite the constant improvements in this field, there still exist diverse factors to overcome before achieving a natural control. In particular, the high variability of the brain signals often leads to the incorrect decoding of the subject intentions, producing unreliable behaviors in the controlled device. A possible solution to this problem would be that of correcting this erroneous decoding using a feedback signal from the user. In this work, we evaluate the possibility of decoding neural signals associated to performance monitoring (EEG-recorded error-related potentials) during a reaching task. Compared to previous works where these error potentials were recorded under scenarios with discrete movements performed by the cursor, under real conditions the cursor is moving continuously and thus the system is required to asynchronously detect any possible error. To this end, we simulated two different erroneous events during the monitoring of a reaching task: errors at the beginning of the movement, and errors happening in the middle of the trajectory being executed. Through the analysis of the recorded EEG of three subjects, we demonstrate the existence of neural correlates for the two types of elicited error potentials, and we are able to asynchronously detect them with high accuracies.

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Section: Discussionmentioning

confidence: 95%

Asynchronous Decoding of Error Potentials during the Monitoring of a Reaching Task

Omedes

Iturrate

Chavarriaga

et al. 2015

2015 IEEE International Conference on Systems, Man, and Cybernetics

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“…A statistical significance test based on bootstrapping was run over the ERSs following the method described in [20]. O2 P8 P4 CP6 T8 C4 FC6 F8 F4 AF4 Fp2 Oz Pz CP2 CPz CP1 Cz FC2 FCz FC1 Fz O1 P7 P3 CP5 T7 C3 FC5 F7 F3 AF3 P8 P4 CP6 T8 C4 FC6 F8 F4 AF4 Fp2 Oz Pz CP2 CPz CP1 Cz FC2 FCz FC1 Fz O1 P7 P3 CP5 T7 C3 FC5 F7 F3 AF3 Fp1 alpha band (8)(9)(10)(11)(12) Fz O1 P7 P3 CP5 T7 C3 FC5 F7 F3 AF3 O2 P8 P4 CP6 T8 C4 FC6 F8 F4 AF4 Fp2 Oz Pz CP2 CPz CP1 Cz FC2 FCz FC1 Fz O1 P7 P3 CP5 T7 C3 FC5 F7 F3 AF3 P8 P4 CP6 T8 C4 FC6 F8 F4 AF4 Fp2 Oz Pz CP2 CPz CP1 Cz FC2 FCz FC1 Fz O1 P7 P3 CP5 T7 C3 FC5 F7 F3 AF3 Fp1 P8 P4 CP6 T8 C4 FC6 F8 F4 AF4 Fp2 Oz Pz CP2 CPz CP1 Cz FC2 FCz FC1 Fz O1 P7 P3 CP5 T7 C3 FC5 F7 F3 AF3 Fp1 CENTRAL REGION LEFT HEMISPHERE RIGHT HEMISPHERE CENTRAL REGION LEFT HEMISPHERE RIGHT HEMISPHERE CENTRAL REGION LEFT HEMISPHERE RIGHT HEMISPHERE Fig. 2.…”

Section: Analysis Of Continuous Error Evaluationmentioning

confidence: 99%

“…Furthermore, they rely on discrete tasks, where the signals were detected in a synchronous fashion. A recent work has shown that it is possible to asynchronously detect errors during tasks where a device performs continuous trajectories using ECoG [10]. In this asynchronous setting, there are no explicit correct events since the device is continuously moving.…”

mentioning

confidence: 99%

Brain connectivity in continuous error tasks

Omedes

Iturrate

Montesano

2014

2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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Abstract-Error-related potentials (ErrP) have been recently incorporated in brain-machine interfaces (BMIs) due to its ability to adapt and correct both the output of the BMI or the behavior of the machine. Most of these applications rely on synchronous tasks with different user's evaluations associated to correct and wrong events. Asynchronous detection during the continuous evaluation of the task, however, has to cope with background noise and an increased number of misdetections common in event-related potential detection. This paper studies a different characteristic that may carry additional information to be exploited by asynchronous ErrP detectors: brain connectivity coherence patterns appearing while the user monitors the continuous operation of a device. The results obtained with five subject revealed the presence of an error potential in an asynchronous reaching task an showed an increase in the coherency within the theta band.

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“…1,2,6-8, 10,11,[19][20][21][22][23][24][25][26][27][28][29]35 On the other hand, there is a relative dearth of SEEG-related BMI studies. 18,30 Previous SEEG BMI studies have demonstrated the possibility of using SEEG electrodes in communication BMIs in which letters were selected by involuntary brain responses.…”

Section: Discussionmentioning

confidence: 99%

“…32 Whereas SEEG was originally described by Bancaud et al in 1965, 3 it has only become increasingly used in the US since 2009. 14,32 Brain-machine interfacing is an area of research that is increasingly taking place in tandem with invasive brain monitoring for epilepsy, 1,2,[6][7][8]10,11,[18][19][20][21][22][23][24][25][26][27][28][29][30]35 even though BMI systems are being developed primarily for paralyzed individuals. Brain-machine interface systems "decode" some aspect of brain processing in real time and use that decoded information to control an assistive device.…”

mentioning

confidence: 99%

Stereoelectroencephalography for continuous two-dimensional cursor control in a brain-machine interface

Vadera¹,

Marathe

González-Martínez³

et al. 2013

FOC

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Stereoelectroencephalography (SEEG) is becoming more prevalent as a planning tool for surgical treatment of intractable epilepsy. Stereoelectroencephalography uses long, thin, cylindrical “depth” electrodes containing multiple recording contacts along each electrode's length. Each lead is inserted into the brain percutaneously. The advantage of SEEG is that the electrodes can easily target deeper brain structures that are inaccessible with subdural grid electrodes, and SEEG does not require a craniotomy. Brain-machine interface (BMI) research is also becoming more common in the Epilepsy Monitoring Unit. A brain-machine interface decodes a person's desired movement or action from the recorded brain activity and then uses the decoded brain activity to control an assistive device in real time. Although BMIs are primarily being developed for use by severely paralyzed individuals, epilepsy patients undergoing invasive brain monitoring provide an opportunity to test the effectiveness of different invasive recording electrodes for use in BMI systems. This study investigated the ability to use SEEG electrodes for control of 2D cursor velocity in a BMI. Two patients who were undergoing SEEG for intractable epilepsy participated in this study. Participants were instructed to wiggle or rest the hand contralateral to their SEEG electrodes to control the horizontal velocity of a cursor on a screen. Simultaneously they were instructed to wiggle or rest their feet to control the vertical component of cursor velocity. The BMI system was designed to detect power spectral changes associated with hand and foot activity and translate those spectral changes into horizontal and vertical cursor movements in real time. During testing, participants used their decoded SEEG signals to move the brain-controlled cursor to radial targets that appeared on the screen. Although power spectral information from 28 to 32 electrode contacts were used for cursor control during the experiment, post hoc analysis indicated that better control may have been possible using only a single SEEG depth electrode containing multiple recording contacts in both hand and foot cortical areas. These results suggest that the advantages of using SEEG for epilepsy monitoring may also apply to using SEEG electrodes in BMI systems. Specifically, SEEG electrodes can target deeper brain structures, such as foot motor cortex, and both hand and foot areas can be targeted with a single SEEG electrode implanted percutaneously. Therefore, SEEG electrodes may be an attractive option for simple BMI systems that use power spectral modulation in hand and foot cortex for independent control of 2 degrees of freedom.

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Error-related electrocorticographic activity in humans during continuous movements

Cited by 52 publications

References 62 publications

Asynchronous Decoding of Error Potentials during the Monitoring of a Reaching Task

Asynchronous Decoding of Error Potentials during the Monitoring of a Reaching Task

Brain connectivity in continuous error tasks

Stereoelectroencephalography for continuous two-dimensional cursor control in a brain-machine interface

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