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Independent component analysis (ICA) has been successfully employed in the study of single-trial evoked potentials (EPs). In this paper, we present an iterative temporal ICA methodology that processes multielectrode single-trial EPs, one channel at a time, in contrast to most existing methodologies which are spatial and analyze EPs from all recording channels simultaneously. The proposed algorithm aims at enhancing individual components in an EP waveform in each single trial, and relies on a dynamic template to guide EP estimation. To quantify the performance of this method, we carried out extensive analyses with artificial EPs, using different models for EP generation, including the phase-resetting and the classical additive-signal models, and several signal-to-noise ratios and EP component latency jitters. Furthermore, to validate the technique, we employed actual recordings of the auditory N100 component obtained from normal subjects. Our results with artificial data show that the proposed procedure can provide significantly better estimates of the embedded EP signals compared to plain averaging, while with actual EP recordings, the procedure can consistently enhance individual components in single trials, in all subjects, which in turn results in enhanced average EPs. This procedure is well suited for fast analysis of very large multielectrode recordings in parallel architectures, as individual channels can be processed simultaneously on different processors. We conclude that this method can be used to study the spatiotemporal evolution of specific EP components and may have a significant impact as a clinical tool in the analysis of single-trial EPs.

The N100 component of the auditory evoked potential (EP) has been recently used to study sensory gating deficits in schizophrenia subjects compared to normal controls. Previously, we used selective averaging to show phase synchronization differences in brain activity between the two populations. In this study, we employed our recently developed iterative independent component analysis (iICA) procedure to measure single-trial EPs in the context of a double-stimulus paradigm. Using the amplitude and latency of the N100 components of the first and second stimuli responses obtained from iICA and four different classification algorithms we were able to accurately classify subjects with 100% sensitivity and 100% specificity. In contrast, the same amplitude and latency features computed from average EPs provided only 69% classification accuracy, with 63% sensitivity and 75% specificity, respectively. We conclude that inter-trial temporal variability plays a significant role in the well-known sensory gating deficits found in schizophrenia patients.

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Abstract. The proliferation of low power and low cost continuous sensing technology is enabling new and innovative applications in wearables and Internet of Things (IoT). At the same time, new applications are creating challenges to maintain real-time response in a resource-constrained device, while maintaining an acceptable performance. In this paper, we describe an IMU (Inertial Measurement Unit) sensor-based generalized hand gesture recognition system, its applications, and the challenges involved in implementing the algorithm in a resource-constrained device. We have implemented a simple algorithm for gesture spotting that substantially reduces the false positives. The gesture recognition model was built using the data collected from 52 unique subjects. The model was mapped onto Intel ® Quark TM SE Pattern Matching Engine, and fieldtested using 8 additional subjects achieving 92% performance.