Spatiotemporal evolution of synchrony dynamics among neuronal populations plays an important role in decoding complicated brain function in normal cognitive processing as well as during pathological conditions such as epileptic seizures. In this paper, a non-linear analytical methodology is proposed to quantitatively evaluate the phase-synchrony dynamics in epilepsy patients. A set of finite neuronal oscillators was adaptively extracted from a multi-channel electrocorticographic (ECoG) dataset utilizing noise-assisted multivariate empirical mode de-composition (NA-MEMD). Next, the instantaneous phases of the oscillatory functions were extracted using the Hilbert transform in order to be utilized in the mean-phase coherence analysis. The phase-synchrony dynamics were then assessed using eigenvalue decomposition. The extracted neuronal oscillators were grouped with respect to their frequency range into wideband (1–600 Hz), ripple (80–250 Hz), and fast-ripple (250–600 Hz) bands in order to investigate the dynamics of ECoG activity in these frequency ranges as seizures evolve. Drug-refractory patients with frontal and temporal lobe epilepsy demonstrated a reduction in phase-synchrony around seizure onset. However, the network phase-synchrony started to increase towards seizure end and achieved its maximum level at seizure offset for both types of epilepsy. This result suggests that hyper-synchronization of the epileptic network may be an essential self-regulatory mechanism by which the brain terminates seizures.

In this paper, a wearable, battery-powered, low-power, low-size, cost-efficient, fully programmable neural stimulator is presented. The system comprises a wearable stimulator module and an external controller. To receive the settings required for the operation of the system, the wearable module is programmed through wireless connection to the external controller. Implemented using off-the-shelf components, the wearable neural stimulator weighs 60 g and measures 9 cm × 5 cm × 2 cm. The system is capable of generating independent biphasic stimulations on 8 channels with programmable amplitudes and timings. The neural stimulator consumes about 1.5 mW in the power-down mode and about 51.2 mW in the active mode when all the 8 channels are active. For in-vivo experiments, the system was used to stimulate motor cortex of an anesthetized rat fixed in a stereotaxic instrument.

In this paper, a novel folded cascode operational amplifier is proposed which improves DC-gain using positive feedback technique. This method does not affect the unity-gain frequency, stability, power dissipation, and output voltage swing of the conventional folded cascode Op-Amp. The proposed OpAmp was designed in a standard 0.18µm TSMC 1.8V CMOS technology. Simulation results show a DC-gain enhancement of 25dB and 513MHz unity gain bandwidth for the presented OpAmp. HSPICE simulation results confirm the theoretical estimated improvements.

Objective: For more than 25 million drug-resistant epilepsy patients, surgical intervention aiming at resecting brain regions where seizures arise is often the only alternative therapy. However, the identification of this epileptogenic zone (EZ) is often imprecise which may affect post-surgical outcomes (PSOs). Interictal high-frequency oscillations (HFOs) have been revealed to be reliable biomarkers in delineating EZ. In this paper, an analytical methodology aiming at automated detection and classification of interictal HFOs is proposed to improve the identification of EZ. Furthermore, the detected high-rate HFO areas were compared with the seizure onset zones (SOZs) and resected areas to investigate their clinical relevance in predicting PSOs. Methods: FIR band-pass filtering as well as a combination of time-series local energy, peak, and duration analysis were utilized to identify high-rate HFO areas in interictal, multichannel intracranial electroencephalographic (iEEG) recordings. The detected HFOs were then classified into fast-ripple (FR), ripple (R), and fast-ripple concurrent with ripple (FRandR) events. Results: The proposed method resulted in sensitivity of 91.08% and false discovery rate of 7.32%. Moreover, it was found that the detected HFO-FRandR areas in concordance with the SOZs would have better delineated the EZ for each patient, while limiting the area of the brain required to be resected. Conclusion: Testing on a dataset of 20 patients has supported the feasibility of using this method to provide an automated algorithm to better delineate the EZ. Significance: The proposed methodology may significantly improve the precision by which pathological brain tissue can be identified.Index Terms-Epilepsy, high-frequency oscillations (HFOs), false-discovery rate (FDR), epileptogenic zone, epilepsy surgery, sensitivity.

For the more than 15 million patients who have drug-resistant epilepsy, surgical resection of the region where seizure arise is often the only alternative therapy. However, the identification of this epileptogenic zone (EZ) is often imprecise. Generally, too little EZ identification and resection may cause seizures to continue and too much resection of it may lead to unnecessary neurological deficits. In this paper, an analytical methodology based on empirical mode decomposition (EMD) is proposed to improve the localization of the EZ for epilepsy patients. In this method, the instantaneous energy of interictal high frequency oscillations (HFOs), extracted from intracranial EEG (iEEG) recordings, are utilized as biomarkers for the EZ identification. The proposed method may significantly improve the precision by which pathological brain tissue is identified.

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This paper reports on the design of a programmable, high output impedance, large voltage compliance microstimulator for low-voltage biomedical applications. A 6-bit binary-weighted digital to analog converter (DAC) is used to generate biphasic stimulus current pulses. A compact current mirror with large output voltage compliance and high output resistance conveys the current pulses to the target tissue. Designed and simulated in a standard 0.18µm CMOS process, the microstimulator circuit is capable of delivering a maximum stimulation current of 160µA to a 10-kΩ resistive load. Operated at a 1.8-V supply voltage, the output stage exhibits a voltage compliance of 1.69V and output resistance of 160MΩ at full scale stimulus current. Layout of the core microelectrode circuit measures 25.5µm×31.5µm.

In this paper, an adaptive, non-linear, analytical methodology is proposed in order to quantitatively evaluate the instantaneous phase-synchrony dynamics in epilepsy patients. A group of finite neuronal oscillators is extracted from a multi­channel electrocorticographic (ECoG) data, using the empirical mode decomposition (EMD). The instantaneous phases of the extracted oscillators are measured using the Hilbert transform in order to be utilized in the mean-phase coherence analysis. Finally, the dynamical evolution of phase-synchrony among the extracted neuronal oscillators within 1–600 Hz frequency range is assessed using eigenvalue decomposition. A different phase­synchrony dynamics was observed in two patients with frontal vs. temporal lobe epilepsy, as their seizures evolve. However, experimental results demonstrated a hypersynchrony level at seizure offset for both types of epilepsy during the ictal periods. This result suggests that hypersynchronization of the epileptic network may be a crucial, self-regulatory mechanism by which the brain terminate seizures.