Modern Techniques in ERP Research

Lange, D.H.; Inbar, G.F.

doi:10.1007/978-3-642-58552-4_36

Cited by 9 publications

(10 citation statements)

References 39 publications

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“…Conversely, estimation of MRPs using a small number of trials is very difficult because of the negative signal-to-noise ratio (SNR) at which they are recorded. There are many attempts to cope with this problem and to reduce the number of repetitions (surveyed in Lange and Inbar (1999)). Unfortunately, most of these techniques do not show the expected results due to the very low SNR.…”

Section: Introductionmentioning

confidence: 99%

On the effects of adaptation to changing loads on movement-related EEG potentials

2005

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Movement-related potentials (MRP), a component of the electroencephalogram (EEG) generated during voluntary movements, are known to vary during adaptation to changing loads and to different load types. This study attempts to reveal these changes. A novel denoising algorithm based on iterative approximation was applied to the MRPs recorded from four subjects while performing simple movements against changing loads. The results show that when subjects perform a repetitive task under a constant load there appears a significant peak in the activity of several MRP components recorded over the prefrontal cortex during the third and fourth repetition of the task. Furthermore, different types of loads do not affect the shape of the MRP but different force intensities do.

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Section: Introductionmentioning

confidence: 99%

On the effects of adaptation to changing loads on movement-related EEG potentials

2005

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“…O,, so that LFASD and matched filter are expected to give similar performances signal. Unfortunately, MRPs are never identical and can vary quite significantly from one recording to the next (LANGE and INBAR, 1999). This means that the signal to be detected is not deterministic but rather quasi-deterministic, and the matched filter cannot achieve optimality as a detector.…”

Section: S(t)mentioning

confidence: 93%

“…2 for different SNR values as a function of~. in MRPs, the SNR is between -20 dB and -10 dB (usually in the vicinity of -15 dB (LANGE and INBAR, 1999)). According to measurements made on the data recorded in the experiments reported below, ~ is between 0.8 and 2.…”

Section: S(t)mentioning

confidence: 99%

“…This measure of success is similar to the one used in HUGGINS et al (1996), although, in that study, a less constrained interval of 1 s before or after the trial was used. it is necessary to identify the movement in a time interval rather than a single time instant, because latency changes are known to occur in MRPs (LANGE and INBAR, 1999). Multiple detections in the range of 0.5 s before or after a true detection were counted as one true detection.…”

Section: Data Processingmentioning

confidence: 99%

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Detection of movement-related potentials from the electro-encephalogram for possible use in a brain-computer interface

Yom-Tov

Inbar

2003

Med. Biol. Eng. Comput.

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Brain-computer interfaces are devices for enabling patients with severe motor disorders to communicate with the world. One method for operating such devices is to use movement-related potentials that are generated in the brain when the patient moves, or imagines a movement of, one of his limbs. An algorithm for detecting movement-related potentials using a small number of EEG channels was developed. This algorithm is a combination of the matched filter, a non-linear transformation previously developed as part of a similar detector, and a classifier. The algorithm was compared with previous designs of similar detectors by both theoretic analysis and off-line evaluation of performance on data recorded from five subjects. It is shown that the performance of the algorithm was superior to that of previous methods, improving the area under the receiver operating characteristic curve to 87.8%, an improvement of 25% compared with the best previously suggested detection method. Finally, the probable sources for false detections were identified, and possible ways to minimise them are proposed.

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“…Many algorithms have been proposed to detect trialto-trial variability (Bartnik et al 1982;Birch et al 1993;Thakor 1993;Lange and Inbar 1999). Most of these algorithms work well for cognitive evoked potentials with a non-negative SNR, but fail when applied to MRPs which have a highly negative SNR.…”

Section: Introductionmentioning

confidence: 99%

Movement-related potentials during the performance of a motor task II: Cerebral areas activated during learning of the task

Yom-Tov

Grossman

Inbar

2001

Biological Cybernetics

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Movement-related potentials (MRPs) recorded from the brain are thought to vary during learning of a motor task. However, since MRPs are recorded at a very low signal-to-noise ratio, it is difficult to measure these variations. In this study we attempt to remove most of the accompanying noise thus enabling the tracking of transient phenomena in MRPs recorded during learning of a motor task. Subjects performed a simple motor task which required learning. A modified version of the matching pursuit algorithm was used in order to remove a significant portion of the electroencephalographic noise overlapping the MRPs recorded in the experiment. Small groups of MRPs were then averaged according to experimental parameters. Our results show that the power of the MRPs does not decay uniformly during learning. Instead, there is a significant peak in their power after 4 or 5 repetitions of the task. This peak is noticeable especially in electrodes placed over the prefrontal region of the cortex at times subsequent to the actual movement. The observed pattern of activity may indicate problem solving related to comprehension of the force against which the user performed the task. It is possible that this problem solving occurs in the prefrontal cortex.

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Modern Techniques in ERP Research

Cited by 9 publications

References 39 publications

On the effects of adaptation to changing loads on movement-related EEG potentials

On the effects of adaptation to changing loads on movement-related EEG potentials

Detection of movement-related potentials from the electro-encephalogram for possible use in a brain-computer interface

Movement-related potentials during the performance of a motor task II: Cerebral areas activated during learning of the task

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