Ultrahigh-Density 256-Channel Neural Sensing Microsystem Using TSV-Embedded Neural Probes.

Highly integrated neural sensing microsystems are crucial to capture accurate signals for brain function investigations. In this paper, a 256-channel neural sensing microsystem with a sensing area of 5 × 5 mm is presented based on 2.5-D through-silicon-via (TSV) integration.

Brain Dynamics in Predicting Driving Fatigue Using a Recurrent Self-Evolving Fuzzy Neural Network.

This paper proposes a generalized prediction system called a recurrent self-evolving fuzzy neural network (RSEFNN) that employs an on-line gradient descent learning rule to address the electroencephalography (EEG) regression problem in brain dynamics for driving fatigue. The cognitive states of drivers significantly affect driving safety; in particular, fatigue driving, or drowsy driving, endangers both the individual and the public. For this reason, the development of brain-computer interfaces (BCIs) that can identify drowsy driving states is a crucial and urgent topic of study.

A double-sided, single-chip integration scheme using through-silicon-via for neural sensing applications.

We present a new double-sided, single-chip monolithic integration scheme to integrate the CMOS circuits and MEMS structures by using through-silicon-via (TSV). Neural sensing applications were chosen as the implementation example. The proposed heterogeneous device integrates standard 0.

2.5D heterogeneously integrated microsystem for high-density neural sensing applications.

Heterogeneously integrated and miniaturized neural sensing microsystems are crucial for brain function investigation. In this paper, a 2.5D heterogeneously integrated bio-sensing microsystem with μ-probes and embedded through-silicon-via (TSVs) is presented for high-density neural sensing applications.

An Interval Type-2 Neural Fuzzy System for Online System Identification and Feature Elimination.

We propose an integrated mechanism for discarding derogatory features and extraction of fuzzy rules based on an interval type-2 neural fuzzy system (NFS)-in fact, it is a more general scheme that can discard bad features, irrelevant antecedent clauses, and even irrelevant rules. High-dimensional input variable and a large number of rules not only enhance the computational complexity of NFSs but also reduce their interpretability. Therefore, a mechanism for simultaneous extraction of fuzzy rules and reducing the impact of (or eliminating) the inferior features is necessary.

Controlling a human-computer interface system with a novel classification method that uses electrooculography signals.

Electrooculography (EOG) signals can be used to control human-computer interface (HCI) systems, if properly classified. The ability to measure and process these signals may help HCI users to overcome many of the physical limitations and inconveniences in daily life. However, there are currently no effective multidirectional classification methods for monitoring eye movements.

Design of the multi-channel electroencephalography-based brain-computer interface with novel dry sensors.

The traditional brain-computer interface (BCI) system measures the electroencephalography (EEG) signals by the wet sensors with the conductive gel and skin preparation processes. To overcome the limitations of traditional BCI system with conventional wet sensors, a wireless and wearable multi-channel EEG-based BCI system is proposed in this study, including the wireless EEG data acquisition device, dry spring-loaded sensors, a size-adjustable soft cap. The dry spring-loaded sensors are made of metal conductors, which can measure the EEG signals without skin preparation and conductive gel.