Background: Over the last few years much research has been devoted to investigating the synchronization between cortical motor and muscular activity as measured by EEG/MEG-EMG coherence. The main focus so far has been on corticomuscular coherence (CMC) during static force condition, for which coherence in beta-range has been described. In contrast, we showed in a recent study [1] that dynamic force condition is accompanied by gamma-range CMC. The modulation of the CMC by various dynamic force amplitudes, however, remained uninvestigated. The present study addresses this question. We examined eight healthy human subjects. EEG and surface EMG were recorded simultaneously. The visuomotor task consisted in isometric compensation for 3 forces (static, small and large dynamic) generated by a manipulandum. The CMC, the cortical EEG spectral power (SP), the EMG SP and the errors in motor performance (as the difference between target and exerted force) were analyzed. Results: For the static force condition we found the well-documented, significant beta-range CMC (15–30 Hz) over the contralateral sensorimotor cortex. Gamma-band CMC (30–45 Hz) occurred in both small and large dynamic force conditions without any significant difference between both conditions. Although in some subjects beta-range CMC was observed during both dynamic force conditions no significant difference between conditions could be detected. With respect to the motor performance, the lowest errors were obtained in the static force condition and the highest ones in the dynamic condition with large amplitude. However, when we normalized the magnitude of the errors to the amplitude of the applied force (relative errors) no significant difference between both dynamic conditions was observed. Conclusion: These findings confirm that during dynamic force output the corticomuscular network oscillates at gamma frequencies. Moreover, we show that amplitude modulation of dynamic force has no effect on the gamma CMC in the low force range investigated. We suggest that gamma CMC is rather associated with the internal state of the sensorimotor system as supported by the unchanged relative error between both dynamic conditions.

In this paper we perform an analysis of human behaviour for people standing in a queue with the aim to discover, in an unsupervised way, ongoing unusual or suspicious activities. The main activity types we focus on are detecting people loitering around the queue and people going against the flow of the queue or undertaking a suspicious path. The proposed approach works by first detecting and tracking moving individuals from a stereo depth map in real time. Activity zones (including queue zones) are then automatically learnt employing a soft computing-based algorithm which takes as input the trajectory of detected mobile objects. Statistical properties on zone occupancy and transition between zones makes it possible to discover abnormalities without the need to learn abnormal models beforehand. The approach has been tested on a dataset realistically representing a border crossing and its environment. The current results suggest that the proposed approach constitutes a robust knowledge discovery tool able to extract queue abnormalities.