Bimodal event-related potentials (ERPs), together with evoked potentials (EPs), measures of motor speed (tapping test, EMG latencies and reaction times (RT)), and psychometric test results were studied in a group of 30 multiple sclerosis (MS) patients and 19 controls. ERPs have been advocated as objective tests of cognitive function. In the present study ERPs were compared with the results of psychometric tests, which have a proven validity in measuring aspects of cognitive function that are important in daily life. Abnormal EMG, RT and tapping speed confirmed that motor aspects of performance were slowed in the MS group. In contrast, cognitive non-motor variables such as Raven-IQ and MQ were not significantly abnormal. The proportions of abnormal ERP N2 and P3 latencies did not differ between the groups. It is concluded that the slow performance of MS subjects is therefore most likely not due to cognitive speed decrement, but to motor, executive impairments. No significant relationships between ERP latencies and psychometric test results were found. This held even for a subgroup of 5 MS patients with psychometrically established cognitive impairments. Based on these results, we query the relevance of ERPs as subtle indicators of cognitive impairment in MS.
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http://dx.doi.org/10.1016/0022-510x(92)90088-3 | DOI Listing |
Imaging Neurosci (Camb)
April 2024
Department of Electrical Engineering, Columbia University, New York, NY, United States.
Listeners with hearing loss have trouble following a conversation in multitalker environments. While modern hearing aids can generally amplify speech, these devices are unable to tune into a target speaker without first knowing to which speaker a user aims to attend. Brain-controlled hearing aids have been proposed using auditory attention decoding (AAD) methods, but current methods use the same model to compare the speech stimulus and neural response, regardless of the dynamic overlap between talkers which is known to influence neural encoding.
View Article and Find Full Text PDFClin Neurophysiol Pract
December 2024
NeuRAL Lab, Abbott Neuromodulation, Plano, TX 75024, USA.
Objective: This study aims to investigate the sources of later response in epidural spinal recordings (ESRs) obtained from implanted leads during spinal cord stimulation, a topic has not been widely studied in previous research.
Methods: Two patients with lower back and lower extremity pain underwent SCS implantation with intraoperative neuromonitoring (IONM). The timing of extracted peaks in ESRs and intramuscular electromyography (EMG) recordings were analyzed and compared to a Monte Carlo simulation for synchronization analysis.
J Clin Med Res
January 2025
Department of Rehabilitation Medicine, Affiliated Hospital of Guilin Medical University, Guilin 541001, Guangxi, China.
Background: Transcranial static magnetic stimulation (tSMS) as a new noninvasive brain stimulation (NIBS) technique is gradually gaining widespread attention. This study aims to investigate the effects of tSMS on the excitability of the somatosensory cortex in healthy adults.
Methods: Forty healthy volunteers were recruited and randomly assigned to either the intervention group (tSMS) or the control group (sham), with 20 participants in each.
Psychophysiology
January 2025
Department of Psychology, University of Georgia, Athens, Georgia, USA.
Emotional experiences involve dynamic multisensory perception, yet most EEG research uses unimodal stimuli such as naturalistic scene photographs. Recent research suggests that realistic emotional videos reliably reduce the amplitude of a steady-state visual evoked potential (ssVEP) elicited by a flickering border. Here, we examine the extent to which this video-ssVEP measure compares with the well-established Late Positive Potential (LPP) that is reliably larger for emotional relative to neutral scenes.
View Article and Find Full Text PDFNeuroimage
January 2025
Department of Computer Science, University of Innsbruck, Technikerstrasse 21a, Innsbruck, 6020, Austria. Electronic address:
The objective of this study is to assess the potential of a transformer-based deep learning approach applied to event-related brain potentials (ERPs) derived from electroencephalographic (EEG) data. Traditional methods involve averaging the EEG signal of multiple trials to extract valuable neural signals from the high noise content of EEG data. However, this averaging technique may conceal relevant information.
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