A Review of Techniques for Detection of Movement Intention Using Movement-Related Cortical Potentials <s> Factors Influencing BP. <s> Abstract Since discovery of the slow negative electroencephalographic (EEG) activity preceding self-initiated movement by Kornhuber and Deecke [Kornhuber HH, Deecke L. Hirnpotentialanderungen bei Willkurbewegungen und passiven Bewegungen des Menschen: Bereitschaftspotential und reafferente Potentiale. Pflugers Archiv 1965;284:1–17], various source localization techniques in normal subjects and epicortical recording in epilepsy patients have disclosed the generator mechanisms of each identifiable component of movement-related cortical potentials (MRCPs) to some extent. The initial slow segment of BP, called ‘early BP’ in this article, begins about 2 s before the movement onset in the pre-supplementary motor area (pre-SMA) with no site-specificity and in the SMA proper according to the somatotopic organization, and shortly thereafter in the lateral premotor cortex bilaterally with relatively clear somatotopy. About 400 ms before the movement onset, the steeper negative slope, called ‘late BP’ in this article (also referred to as NS′), occurs in the contralateral primary motor cortex (M1) and lateral premotor cortex with precise somatotopy. These two phases of BP are differentially influenced by various factors, especially by complexity of the movement which enhances only the late BP. Event-related desynchronization (ERD) of beta frequency EEG band before self-initiated movements shows a different temporospatial pattern from that of the BP, suggesting different neuronal mechanisms for the two. BP has been applied for investigating pathophysiology of various movement disorders. Volitional motor inhibition or muscle relaxation is preceded by BP quite similar to that preceding voluntary muscle contraction. Since BP of typical waveforms and temporospatial pattern does not occur before organic involuntary movements, BP is used for detecting the participation of the ‘voluntary motor system’ in the generation of apparently involuntary movements in patients with psychogenic movement disorders. In view of Libet et al.’s report [Libet B, Gleason CA, Wright EW, Pearl DK. Time of conscious intention to act in relation to onset of cerebral activity (readiness-potential). The unconscious initiation of a freely voluntary act. Brain 1983;106:623–642] that the awareness of intention to move occurred much later than the onset of BP, the early BP might reflect, physiologically, slowly increasing cortical excitability and, behaviorally, subconscious readiness for the forthcoming movement. Whether the late BP reflects conscious preparation for intended movement or not remains to be clarified. <s> BIB001 </s> A Review of Techniques for Detection of Movement Intention Using Movement-Related Cortical Potentials <s> Factors Influencing BP. <s> The paper presents a novel pattern recognition approach for the classification of single-trial movement-related cortical potentials (MRCPs) generated by variations of force-related parameters during voluntary tasks. The feature space was built from the coefficients of a discrete dyadic wavelet transformation. Mother wavelet parameterization allowed the tuning of basis functions to project the signals. The mother wavelet was optimized to minimize the classification error estimated from the training set. Classification was performed with a support vector machine (SVM) approach with optimization of the width of a Gaussian kernel and of the regularization parameter. The efficacy of the optimization procedures was representatively shown on electroencephalographic recordings from two subjects who performed unilateral isometric plantar flexions at two target torques and two rates of torque development. The proposed classification method was tested on four pairs of classes corresponding to the change in only one of the two parameters of the task. Misclassification rate (test set) in the classification of 1-s EEG activity immediately before the onset of the tasks was reduced from 50.8+/-2.9% with worst wavelet and nearest representative classifier, to 40.2+/-7.3% with optimal wavelet and nearest representative classifier, and to 15.8+/-3.4% with optimal wavelet and SVM with optimization of the kernel and regularization parameter. The proposed pattern recognition method is promising for classification of MRCPs modulated by variations of force-related parameters. <s> BIB002 </s> A Review of Techniques for Detection of Movement Intention Using Movement-Related Cortical Potentials <s> Factors Influencing BP. <s> The study investigates the accuracy in discriminating rate of torque development (RTD) and target torque (TT) (task parameters) from electroencephalography (EEG) signals generated during imaginary motor tasks. Signals were acquired from nine healthy subjects during four imaginary isometric plantar-flexions of the right foot involving two RTDs (ballistic and moderate) and two TTs (30 and 60% of the maximal voluntary contraction torque), each repeated 60 times in random order. The single-trial EEG traces were classified with a pattern recognition approach based on wavelet coefficients as features and support vector machine (SVM) as classifier. Average misclassification rates were (mean ± SD) 16 ± 9% and 26 ± 13% for discrimination of the two TTs under ballistic and moderate RTDs, respectively. RTDs could be discriminated with misclassification rates of 16 ± 11% and 19 ± 10% under high and low TT, respectively. These results indicate that differences in both TT and RTD can be detected from single-trial EEG traces during imaginary tasks. <s> BIB003 </s> A Review of Techniques for Detection of Movement Intention Using Movement-Related Cortical Potentials <s> Factors Influencing BP. <s> Time domain features of slow potentials ≤2 Hz) during periodic movement and motor imagery at fast and slow (4 Hz and 2 Hz) were investigated in 4 healthy subjects by event-related potentials in the paper. EEG was recorded from 64 electrodes and 9 closely spaced electrodes overlaying the left and right sensorimotor area were analyzed. The subjects performed 40 fast and 40 slow (paced by metronome) movement and motor imagery of six tasks that involved three limbs (left and right index fingers and right toes) at first-person perspective. In addition, the subjects also performed motor imagery involved left and right arms and feet at third-person perspective. Based on brain processing mainly involved with the slow potentials during periodic movement and motor imagery, event-related potentials were low-pass filtered with 2 Hz cut-off frequency (24 dB/oct). At C1, Cz, and C2, fast movement-related potentials had a larger positive slow potentials tendency than slow movement-related potentials 1.5 s after movement onset. In contrast to movement, slow motor imagery-related potentials had a larger positive slow potentials tendency than fast motor imagery-related potentials 1.75 s after motor imagery onset at these electrodes. Time delays reaching positive peak and rebound rate after peak were also explored. These results may be very important for further exploring relationship between speed and EEG activity during periodic movement and motor imagery. The study may provide a strategy to realize fine control of robots by brain-controlled robot interface. <s> BIB004 </s> A Review of Techniques for Detection of Movement Intention Using Movement-Related Cortical Potentials <s> Factors Influencing BP. <s> Objective. In this study, the objective was to detect movement intentions and extract different levels of force and speed of the intended movement from scalp electroencephalography (EEG). We then estimated the performance of the closed loop system. Approach. Cued movements were detected from continuous EEG recordings using a template of the initial phase of the movement-related cortical potential in 12 healthy subjects. The temporal features, extracted from the movement intention, were classified with an optimized support vector machine. The system performance was evaluated when combining detection with classification. Main results. The system detected 81% of the movements and correctly classified 75 ± 9% and 80 ± 10% of these at the point of detection when varying the force and speed, respectively. When the detector was combined with the classifier, the system detected and correctly classified 64 ± 13% and 67 ± 13% of these movements. The system detected and incorrectly classified 21 ± 7% and 16 ± 9% of the movements. The movements were detected 317 ± 73 ms before the movement onset. Significance. The results indicate that it is possible to detect movement intentions with limited latencies, and extract and classify different levels of force and speed, which may be combined with assistive technologies for patient-driven neurorehabilitation. <s> BIB005
Components of MRCP can be inspired by various factors such as preparatory state, level of intention, movement selection, pace of movement repetition, speed and precision of movement, praxis movement, perceived effort, force exerted, discreteness and complexity of movement, learning and skill acquisition, and pathological injuries of various brain structures. The review by BIB001 sums up several factors influencing BP. Recently, few studies intended to analyze the effect of kinetics of movement such as force and speed on MRCPs BIB002 BIB003 BIB005 BIB004 .