Less Data Same Information for Event-Based Sensors: A Bioinspired Filtering and Data Reduction Algorithm.

Sensors provide data which need to be processed after acquisition to remove noise and extract relevant information. When the sensor is a network node and acquired data are to be transmitted to other nodes (e.g., through Ethernet), the amount of generated data from multiple nodes can overload the communication channel. The reduction of generated data implies the possibility of lower hardware requirements and less power consumption for the hardware devices. This work proposes a filtering algorithm (LDSI-Less Data Same Information) which reduces the generated data from event-based sensors without loss of relevant information. It is a bioinspired filter, i.e., event data are processed using a structure resembling biological neuronal information processing. The filter is fully configurable, from a "transparent mode" to a very restrictive mode. Based on an analysis of configuration parameters, three main configurations are given: weak, medium and restrictive. Using data from a DVS event camera, results for a similarity detection algorithm show that event data can be reduced up to 30% while maintaining the same similarity index when compared to unfiltered data. Data reduction can reach 85% with a penalty of 15% in similarity index compared to the original data. An object tracking algorithm was also used to compare results of the proposed filter with other existing filter. The LDSI filter provides less error ( 4 . 86 ± 1 . 87 ) when compared to the background activity filter ( 5 . 01 ± 1 . 93 ). The algorithm was tested under a PC using pre-recorded datasets, and its FPGA implementation was also carried out. A Xilinx Virtex6 FPGA received data from a 128 × 128 DVS camera, applied the LDSI algorithm, created a AER dataflow and sent the data to the PC for data analysis and visualization. The FPGA could run at 177 MHz clock speed with a low resource usage (671 LUT and 40 Block RAM for the whole system), showing real time operation capabilities and very low resource usage. The results show that, using an adequate filter parameter tuning, the relevant information from the scene is kept while fewer events are generated (i.e., fewer generated data).

Selection of the Optimal Algorithm for Real-Time Estimation of Beta Band Power during DBS Surgeries in Patients with Parkinson's Disease.

Deep Brain Stimulation (DBS) is a surgical procedure for the treatment of motor disorders in patients with Parkinson's Disease (PD). DBS involves the application of controlled electrical stimuli to a given brain structure. The implantation of the electrodes for DBS is performed by a minimally invasive stereotactic surgery where neuroimaging and microelectrode recordings (MER) are used to locate the target brain structure. The Subthalamic Nucleus (STN) is often chosen for the implantation of stimulation electrodes in DBS therapy. During the surgery, an intraoperative validation is performed to locate the dorsolateral region of STN. Patients with PD reveal a high power in the ß band (frequencies between 13 Hz and 35 Hz) in MER signal, mainly in the dorsolateral region of STN. In this work, different power spectrum density methods were analyzed with the aim of selecting one that minimizes the calculation time to be used in real time during DBS surgery. In particular, the results of three nonparametric and one parametric methods were compared, each with different sets of parameters. It was concluded that the optimum method to perform the real-time spectral estimation of beta band from MER signal is Welch with Hamming windows of 1.5 seconds and 50% overlap.

Regular theta-firing neurons in the nucleus incertus during sustained hippocampal activation.

This paper describes the existence of theta-coupled neuronal activity in the nucleus incertus (NI). Theta rhythm is relevant for cognitive processes such as spatial navigation and memory processing, and can be recorded in a number of structures related to the hippocampal activation including the NI. Strong evidence supports the role of this tegmental nucleus in neural circuits integrating behavioural activation with the hippocampal theta rhythm. Theta oscillations have been recorded in the local field potential of the NI, highly coupled to the hippocampal waves, although no rhythmical activity has been reported in neurons of this nucleus. The present work analyses the neuronal activity in the NI in conditions leading to sustained hippocampal theta in the urethane-anaesthetised rat, in order to test whether such activation elicits a differential firing pattern. Wavelet analysis has been used to better define the neuronal activity already described in the nucleus, i.e., non-rhythmical neurons firing at theta frequency (type I neurons) and fast-firing rhythmical neurons (type II). However, the most remarkable finding was that sustained stimulation activated regular-theta neurons (type III), which were almost silent in baseline conditions and have not previously been reported. Thus, we describe the electrophysiological properties of type III neurons, focusing on their coupling to the hippocampal theta. Their spike rate, regularity and phase locking to the oscillations increased at the beginning of the stimulation, suggesting a role in the activation or reset of the oscillation. Further research is needed to address the specific contribution of these neurons to the entire circuit.

Theta synchronization between the hippocampus and the nucleus incertus in urethane-anesthetized rats.

Oscillatory coupling between distributed areas can constitute a mechanism for neuronal integration. Theta oscillations provide temporal windows for hippocampal processing and only appear during certain active states of animals. Since previous studies have demonstrated that nucleus incertus (NI) contributes to the generation of hippocampal theta activity, in this paper, we evaluated the oscillatory coupling between both structures. We compared hippocampal and NI field potentials that were simultaneously recorded in urethane-anesthetized rats. Electrical and cholinergic stimulations of the reticularis pontis oralis nucleus have been used as hippocampal theta generation models. The spectral analyses reveal that electrical stimulation induced an increase in theta oscillations in both channels, whose frequencies depended on the intensity of stimulation. The intensity range used simultaneously increased the normalized spectral energy in the fast theta band (6-12 Hz) in HPC and NI. Frequencies within the theta range were found to be very similar in both channels. In order to validate coupling, spectral coherence was inspected. The data reveal that coherence in the high theta band also increased while stimuli were applied. Cholinergic activation progressively increased the main frequency in both structures to reach an asymptotic period with stable peak frequency in the low theta range (3-6 Hz), which could be first observed in NI and lasted about 1,500 s. Coherence in this band reached values close to 1. Taken together, these results support an electrophysiological and functional coupling between the hippocampus and the reticular formation, suggesting NI to be part of a distributed network working at theta frequencies.

Foetal ECG recovery using dynamic neural networks.

Non-invasive electrocardiography has proven to be a very interesting method for obtaining information about the foetus state and thus to assure its well-being during pregnancy. One of the main applications in this field is foetal electrocardiogram (ECG) recovery by means of automatic methods. Evident problems found in the literature are the limited number of available registers, the lack of performance indicators, and the limited use of non-linear adaptive methods. In order to circumvent these problems, we first introduce the generation of synthetic registers and discuss the influence of different kinds of noise to the modelling. Second, a method which is based on numerical (correlation coefficient) and statistical (analysis of variance, ANOVA) measures allows us to select the best recovery model. Finally, finite impulse response (FIR) and gamma neural networks are included in the adaptive noise cancellation (ANC) scheme in order to provide highly non-linear, dynamic capabilities to the recovery model. Neural networks are benchmarked with classical adaptive methods such as the least mean squares (LMS) and the normalized LMS (NLMS) algorithms in simulated and real registers and some conclusions are drawn. For synthetic registers, the most determinant factor in the identification of the models is the foetal-maternal signal-to-noise ratio (SNR). In addition, as the electromyogram contribution becomes more relevant, neural networks clearly outperform the LMS-based algorithm. From the ANOVA test, we found statistical differences between LMS-based models and neural models when complex situations (high foetal-maternal and foetal-noise SNRs) were present. These conclusions were confirmed after doing robustness tests on synthetic registers, visual inspection of the recovered signals and calculation of the recognition rates of foetal R-peaks for real situations. Finally, the best compromise between model complexity and outcomes was provided by the FIR neural network. Both the methodology for selecting a model and the introduction of advanced neural models are the main contributions of this paper.