Neuronal oscillations span a wide range of spatial and temporal scales that extend beyond traditional clinical EEG. Recent research suggests that high-frequency oscillations (HFO), in the ripple (80-250 Hz) and fast ripple (250-1000 Hz) frequency range, may be signatures of epileptogenic brain and involved in the generation of seizures. However, most research investigating HFO in humans comes from microwire recordings, whose relationship to standard clinical intracranial EEG (iEEG) has not been explored. In this study iEEG recordings (DC - 9000 Hz) were obtained from human medial temporal lobe using custom depth electrodes containing both microwires and clinical macroelectrodes. Ripple and fast-ripple HFO recorded from both microwires and clinical macroelectrodes were increased in seizure generating brain regions compared to control regions. The distribution of HFO frequencies recorded from the macroelectrodes was concentrated in the ripple frequency range, compared to a broad distribution of HFO frequencies recorded from microwires. The average frequency of ripple HFO recorded from macroelectrodes was lower than that recorded from microwires (143.3 +/- 49.3 Hz versus 116.3 +/- 38.4, Wilcoxon rank sum P<0.0001). Fast-ripple HFO were most often recorded on a single microwire, supporting the hypothesis that fast-ripple HFO are primarily generated by highly localized, sub-millimeter scale neuronal assemblies that are most effectively sampled by microwire electrodes. Future research will address the clinical utility of these recordings for localizing epileptogenic networks and understanding seizure generation.

Warren CP, Hu S, Stead M, Brinkmann BH, Bower MR, Worrell GA. Synchrony in normal and focal epileptic brain: the seizure onset zone is functionally disconnected.

Electroencephalogram (EEG) signals recorded from sensor electrodes on the scalp can directly detect the brain dynamics in response to different emotional states. Emotion recognition from EEG signals has attracted broad attention, partly due to the rapid development of wearable computing and the needs of a more immersive human-computer interface (HCI) environment. To improve the recognition performance, multi-channel EEG signals are usually used. A large set of EEG sensor channels will add to the computational complexity and cause users inconvenience. ReliefF-based channel selection methods were systematically investigated for EEG-based emotion recognition on a database for emotion analysis using physiological signals (DEAP). Three strategies were employed to select the best channels in classifying four emotional states (joy, fear, sadness and relaxation). Furthermore, support vector machine (SVM) was used as a classifier to validate the performance of the channel selection results. The experimental results showed the effectiveness of our methods and the comparison with the similar strategies, based on the F-score, was given. Strategies to evaluate a channel as a unity gave better performance in channel reduction with an acceptable loss of accuracy. In the third strategy, after adjusting channels’ weights according to their contribution to the classification accuracy, the number of channels was reduced to eight with a slight loss of accuracy (58.51% ± 10.05% versus the best classification accuracy 59.13% ± 11.00% using 19 channels). In addition, the study of selecting subject-independent channels, related to emotion processing, was also implemented. The sensors, selected subject-independently from frontal, parietal lobes, have been identified to provide more discriminative information associated with emotion processing, and are distributed symmetrically over the scalp, which is consistent with the existing literature. The results will make a contribution to the realization of a practical EEG-based emotion recognition system.