Electroencephalographic Studies of Skilled Psychomotor Performance

Abstract

Measurements based on the EEG have featured prominently in shaping present-day concepts of the neurocognitive aspects of skilled performance. The techniques include measurements of spectral power, interelectrode coherence, event-related potential components such as the P300, slow potentials, and the method of cognitive inference. The advantages offered by EEG-based approaches lies in their spatiotemporal resolution (potentially 1 mm and less than 1 millisecond, respectively) and the potential to preserve ecological validity, i.e., to obtain measurements of cortical function under the same conditions that the task is normally performed. These studies indicate that activity is reduced in specific regions of the cerebral cortex of experts relative to that observed in novices. These changes occur over time as a result of practice. The authors argue that such cortical change results in less attentional demand and less cognitive interference with motor planning and execution. The findings attest to the plasticity of the central nervous system when one is engaged in goal-directed learning, and hold implications for understanding how the nervous system acquires voluntary skills, whether in the context of the training of an athlete or the rehabilitation of a patient who has lost motor skills due to a disease of the nervous system.

Action observation and motor imagery in performance of complex movements: Evidence from EEG and kinematics analysis

“…Our kinematics and EEG results suggest that better performers were associated with, not only enhancement of activation (stronger alpha ERD) during training, but also with decreased neural activations (greater beta ERS) during movement execution, which might be reflective of greater neural efficiency in the brain networks underlying performance [107,108]. This suggests a more efficient neural code for controlling the motor movement and is consistent with the enhancement of beta synchronization following successful movement [109,110], particularly during gross movements such as the wrist [111], as well as improved motor performance [82].…”

Signal Processing for Non-Invasive Brain Biomarkers of Sensorimotor Performance and Brain Monitoring

“…As previously mentioned, although several methods can be used to isolate some specific frequency bands; one of the main problems of the EEG/MEG spectral analysis is the definition of the upper and lower bounds of the bands (Pfurtscheller & Lopes da Silva, 1999). Although the definition of the frequency band limits can slightly differ from one study to another, a possible approach for partitioning the frequency bands related to human motor performance for healthy adults is to consider the theta ([4-7 Hz]), alpha (8)(9)(10)(11)(12)(13)), beta ) and gamma (36)(37)(38)(39)(40)(41)(42)(43)(44)) frequency bands (e.g., Hatfield et al, 2004;Haufler et al, 2000;Tombini et al, 2009). Sometimes, the frequency range spread from 8 to 15 Hz (Blankertz & Vidaurre, 2009) or from 9 to 13Hz Pfurtscheller & Neuper, 1997) are also named alpha frequency (or mu rhythm under certain conditions).…”

“…Sometimes, the frequency range spread from 8 to 15 Hz (Blankertz & Vidaurre, 2009) or from 9 to 13Hz Pfurtscheller & Neuper, 1997) are also named alpha frequency (or mu rhythm under certain conditions). Moreover, since it has been shown that certain frequency sub-bands are related to specific brain states during a sensorimotor task (e.g., Contreras-Vidal et al, 2004;Gentili et al, 2008;Hatfield et al, 2004;Tombini et al, 2009), most of the EEG/MEG studies refined their analysis by considering sub-frequency bands, typically, the low and high component of the original entire band. Therefore, for the bands previously defined, the low theta ([4-5 Hz]), high theta ([6-7 Hz]), low alpha ([8-10 Hz]), high alpha (11)(12)(13)), low beta (14)(15)(16)(17)(18)(19)(20)(21)(22)(23)) and high beta (24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35)) frequency bands can also be considered.…”

“…Recently, it has been also suggested that measurements of the skin conductance was a better tool to monitor nociceptive stimulation and pain than heart rate and blood pressure (Storm et al, 2008). Another important family of functional biomarkers includes status measurements of brain functions in order to monitor and interpret neural activity, identify specific neurological events and predict outcomes (e.g., Gentili et al, 2008;Guarracino, 2008;Hatfield et al, 2004;Irani et al, 2007;Tuner et al, 2009;van Putten et al, 2005;Williams & Ramamoorthy, 2007). These brain indicators, or brain biomarkers, can be derived from signals recorded by means of invasive acquisition techniques such as implantable microelectrodes arrays or electrocorticography (Schalk et al, 2008), or, alternatively, non-invasive techniques such as electroencephalography (EEG), magnetoencephalography (MEG), functional magnetic resonance imaging (fMRI) or emerging neuroimaging technologies such as functional near infrared spectroscopy (fNIRS) (Irani et al, 2007;Parasuraman & Rizzo, 2007).…”

“…Such assessment in ecological situations requires non-invasive recording of the dynamic brain activity with a high temporal resolution (e.g., millisecond), which is well suited for EEG. Although some research efforts are underway (e.g., Deeny et al, 2003Deeny et al, , 2009Gentili et al, 2008Gentili et al, , 2009aHatfield et al, 2004;Haufler et al, 2000;Kerick et al, 2004) to develop methods to provide non-invasive functional brain biomarkers able to track the brain status during sensorimotor performance; some questions and problems remain. For example, how accurately and efficiently can a cognitive-motor or sensorimotor state be inferred?…”