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We propose electrocorticographic temporal alteration mapping (ETAM) for motor cortex mapping by utilizing movement-related cortical potentials (MRCPs) within the low-frequency band [0.05-3] Hz. This MRCP waveform-based temporal domain approach was compared with the state-of-the-art electrocorticographic frequency alteration mapping (EFAM), which is based on frequency spectrum dynamics. Five patients (two epilepsy cases and three tumor cases) were enrolled in the study. Each patient underwent intraoperative direct electrocortical stimulation (DECS) procedure for motor cortex localization. Moreover, the patients were required to perform simple brisk wrist extension task during awake craniotomy surgery. Cross-validation results showed that the proposed ETAM method had high sensitivity (81.8%) and specificity (94.3%) in identifying sites which exhibited positive DECS motor responses. Moreover, although the sensitivity of the ETAM and EFAM approaches was not significantly different, ETAM had greater specificity compared with EFAM (94.3 vs. 86.1%). These results indicate that for the intraoperative functional brain mapping, ETAM is a promising novel approach for motor cortex localization with the potential to reduce the need for cortical electrical stimulation.

Time-domain feature representation for imagined grip force movement-related cortical potentials (MRCP) of the right or left hand and the decoding of imagined grip force parameters based on electroencephalogram (EEG) activity recorded during a single trial were here investigated. EEG signals were acquired from eleven healthy subjects during four different imagined tasks performed with the right or left hand. Subjects were instructed to execute imagined grip movement at two different levels of force. Each task was executed 60 times in random order. The imagined grip force MRCP of the right or left hand was analyzed by superposition and averaging technology, a single-trial extraction method, analysis of variance (ANOVA), and multiple comparisons. Significantly different features were observed among different imagined grip force tasks. These differences were used to decode imagined grip force parameters using Fisher linear discrimination analysis based on kernel function (k-FLDA) and support vector machine (SVM). Under the proposed experimental paradigm, the study showed that MRCP may characterize the dynamic processing that takes place in the brain during the planning, execution, and precision of a given imagined grip force task. This means that features related to MRCP can be used to decode imagined grip force parameters based on EEG. ANOVA and multiple comparisons of time-domain features for MRCP showed that movement-monitoring potentials (MMP) and specific interval (0-150 ms) average potentials to be significantly different among 4 different imagined grip force tasks. The minimum peak negativity differed significantly between high and low amplitude grip force. Identification of the 4 different imagined grip force tasks based on MMP was performed using k-FLDA and SVM, and the average misclassification rates of 27 % ± 5 % and 24 % ± 4 % across 11 subjects were achieved respectively. The minimum misclassification rate was 15 %, and the average minimum misclassification rate across 11 subjects was 24 % ± 4.5 %. This investigation indicates that imagined grip force MRCP may encode imagined grip force parameters. Single-trial decoding of imagined grip force parameters based on MRCP may be feasible. The study may provide some additional and fine control instructions for brain-computer interfaces. [ABSTRACT FROM AUTHOR]

In recent years, the detection of voluntary motor intentions from electroencephalogram (EEG) has been used for triggering external devices in closed-loop brain–computer interface (BCI) research. Movement-related cortical potentials (MRCP), a type of slow cortical potentials, have been recently used for detection. In order to enhance the efficacy of closed-loop BCI systems based on MRCPs, a manifold method called Locality Preserving Projection, followed by a linear discriminant analysis (LDA) classifier (LPP-LDA) is proposed in this paper to detect MRCPs from scalp EEG in real time. In an online experiment on nine healthy subjects, LPP-LDA statistically outperformed the classic matched filter approach with greater true positive rate (79 ± 11% versus 68 ± 10%; p = 0.007) and less false positives (1.4 ± 0.8/min versus 2.3 ± 1.1/min; p = 0.016). Moreover, the proposed system performed detections with significantly shorter latency (315 ± 165 ms versus 460 ± 123 ms; p = 0.013), which is a fundamental characteristics to induce neuroplastic changes in closed-loop BCIs, following the Hebbian principle. In conclusion, the proposed system works as a generic brain switch, with high accuracy, low latency, and easy online implementation. It can thus be used as a fundamental element of BCI systems for neuromodulation and motor function rehabilitation. [ABSTRACT FROM PUBLISHER]

Abstract: Objective: Human voluntary movement is associated with two changes in electroencephalography (EEG) that can be observed as early as 1.5s prior to movement: slow DC potentials and frequency power shifts in the alpha and beta bands. Our goal was to determine whether and when we can reliably predict human natural movement BEFORE it occurs from EEG signals ONLINE IN REAL-TIME. Methods: We developed a computational algorithm to support online prediction. Seven healthy volunteers participated in this study and performed wrist extensions at their own pace. Results: The average online prediction time was 0.62±0.25s before actual movement monitored by EMG signals. There were also predictions that occurred without subsequent actual movements, where subjects often reported that they were thinking about making a movement. Conclusion: Human voluntary movement can be predicted before movement occurs. Significance: The successful prediction of human movement intention will provide further insight into how the brain prepares for movement, as well as the potential for direct cortical control of a device which may be faster than normal physical control. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents additional evidence showing the persistent functional deficits in concussed athletes as revealed by altered movement-related cortical potentials (MRCP) preceding whole body postural movements at least 30 days post-injury. Eight student-athletes participated in this study (a) prior to injury; and (b) 3, 10 and 30 days after MTBI. EEG was recorded while subjects produced static balance tasks and dynamic postural movements. All subjects were cleared for sport participation within 10 days post-injury based upon neurological and neuropsychological assessments as well as upon clinical symptoms resolution. There was a persistent reduction of MRCP amplitude prior to initiation of postural movement up to 30 days post-injury, although abnormal postural responses basically recovered within 10 days post-injury. The frontal lobe MRCP effects were larger than posterior areas. This supports the notion that behavioral symptoms resolution may not be indicative of brain injury resolution. Overall, persistent alteration of movement-related cortical potentials after MTBI may indicate residual disturbance of neuronal networks involved in preparation and execution of postural movements and a lower threshold for brain re/injury. [Copyright &y& Elsevier]