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 2 is presented based on 2.5-D through-silicon-via (TSV) integration. This microsystem composes of dissolvable µ-needles, TSV-embedded µ-probes, 256-channel neural amplifiers, 11-bit area-power-efficient successive approximation register analog-to-digital converters, and serializers. This microsystem can detect 256 electrocorticography and local field potential signals within a small area of 5 mm × 5 mm. The neural amplifier realizes 57.8 dB gain with only 9.8 µW per channel. The overall power of this microsystem is only 3.79 mW for 256-channel neural sensing. A smaller microsystem with dimension of 6 mm × 4 mm has been also implanted into rat brain for somatosensory evoked potentials (SSEPs) recording by using contralateral and ipsilateral electrical stimuli with intensity from 0.2 to 1.0 mA, and successfully observed different SSEPs from left somatosensory cortex of a rat.

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. Many EEG-based BCIs have been developed as artificial auxiliary systems for use in various practical applications because of the benefits of measuring EEG signals. In the literature, the efficacy of EEG-based BCIs in recognition tasks has been limited by low resolutions. The system proposed in this paper represents the first attempt to use the recurrent fuzzy neural network (RFNN) architecture to increase adaptability in realistic EEG applications to overcome this bottleneck. This paper further analyzes brain dynamics in a simulated car driving task in a virtual-reality environment. The proposed RSEFNN model is evaluated using the generalized cross-subject approach, and the results indicate that the RSEFNN is superior to competing models regardless of the use of recurrent or nonrecurrent structures.

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 0.18 µm CMOS technology, TSV and neural probe array into a compact single chip device. The neural probe array on the back-side of the chip is connected to the CMOS circuits on the front-side of the chip by using low-parasitic TSVs through the chip. Successful fabrication results and detailed characterization demonstrate the feasibility and performance of the neural probe array, TSV and readout circuitry. The fabricated device is 5 × 5 mm(2) in area, with 16 channels of 150 µm-in-length neural probe array on the back-side, 200 µm-deep TSV through the chip and CMOS circuits on the front-side. Each channel consists of a 5 × 6 probe array, 3 × 14 TSV array and a differential-difference amplifier (DDA) based analog front-end circuitry with 1.8 V supply, 21.88 µW power consumption, 108 dB CMRR and 2.56 µVrms input referred noise. In-vivo long term implantation demonstrated the feasibility of presented integration scheme after 7 and 58 days of implantation. We expect the conceptual realization can be extended for higher density recording array by using the proposed method.

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. The proposed approach, namely an interval type-2 Neural Fuzzy System for online System Identification and Feature Elimination (IT2NFS-SIFE), uses type-2 fuzzy sets to model uncertainties associated with information and data in designing the knowledge base. The consequent part of the IT2NFS-SIFE is of Takagi-Sugeno-Kang type with interval weights. The IT2NFS-SIFE possesses a self-evolving property that can automatically generate fuzzy rules. The poor features can be discarded through the concept of a membership modulator. The antecedent and modulator weights are learned using a gradient descent algorithm. The consequent part weights are tuned via the rule-ordered Kalman filter algorithm to enhance learning effectiveness. Simulation results show that IT2NFS-SIFE not only simplifies the system architecture by eliminating derogatory/irrelevant antecedent clauses, rules, and features but also maintains excellent performance.

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. This microsystem is composed of µ-probes with embedded TSVs, 4 dies and a silicon interposer. For capturing 16-channel neural signals, a 24 × 24 µ-probe array with embedded TSVs is fabricated on a 5×5 mm(2) chip and bonded on the back side of the interposer. Thus, each channel contains 6 × 6 µ -probes with embedded TSVs. Additionally, the 4 dies are bonded on the front side of the interposer and designed for biopotential acquisition, feature extraction and classification via low-power analog front-end (AFE) circuits, area-power-efficient analog-to-digital converters (ADCs), configurable discrete wavelet transforms (DWTs), filters, and a MCU. An on-interposer bus ( µ-SPI) is designed for transferring data on the interposer. Finally, the successful in-vivo test demonstrated the proposed 2.5D heterogeneously integrated bio-sensing microsystem. The overall power of this microsystem is only 676.3 µW for 16-channel neural sensing.

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. Here, we describe a classification method used in a wireless EOG-based HCI device for detecting eye movements in eight directions. This device includes wireless EOG signal acquisition components, wet electrodes and an EOG signal classification algorithm. The EOG classification algorithm is based on extracting features from the electrical signals corresponding to eight directions of eye movement (up, down, left, right, up-left, down-left, up-right, and down-right) and blinking. The recognition and processing of these eight different features were achieved in real-life conditions, demonstrating that this device can reliably measure the features of EOG signals. This system and its classification procedure provide an effective method for identifying eye movements. Additionally, it may be applied to study eye functions in real-life conditions in the near future.

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. In addition, the proposed system provides a size-adjustable soft cap that can be used to fit user's head properly. Indeed, the results are shown that the proposed system can properly and effectively measure the EEG signals with the developed cap and sensors, even under movement. In words, the developed wireless and wearable BCI system is able to be used in cognitive neuroscience applications.