🏛️國立陽明交通大學機構典藏

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2.5D Heterogeneously Integrated Microsystem for High-Density Neural Sensing Applications
(2014-12-01)
Huang, Po-Tsang
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Wu, Shang-Lin
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Huang, Yu-Chieh
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Chou, Lei-Chun
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Huang, Teng-Chieh
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 mu-probes and embedded through-silicon-via (TSVs) is presented for high-density neural sensing applications. This microsystem is composed of mu-probes with embedded TSVs, 4 dies and a silicon interposer. For capturing 16-channel neural signals, a 24 x 24 mu-probe array with embedded TSVs is fabricated on a 5 x 5 mm2 chip and bonded on the back side of the interposer. Thus, each channel contains 6 x 6 mu-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 (mu-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 mu W for 16-channel neural sensing.
A Novel 16-Channel Wireless System for Electroencephalography Measurements With Dry Spring-Loaded Sensors
(2014-06-01)
Liao, Lun-De
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Wu, Shang-Lin
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Liou, Chang-Hong
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Lu, Shao-Wei
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Chen, Shi-An
Understanding brain function using electroencephalography (EEG) is an important issue for cerebral nervous system diseases, especially for epilepsy and Alzheimer\'s disease. Many EEG measurement systems are used reliably to study these diseases, but their bulky size and the use of wet sensors make them uncomfortable and inconvenient for users. To overcome the limitations of conventional EEG measurement systems, a wireless and wearable multichannel EEG measurement system is proposed in this paper. This system includes a wireless data acquisition device, dry spring-loaded sensors, and a size-adjustable soft cap. We compared the performance of the proposed system using dry versus conventional wet sensors. A significant positive correlation between readings from wet and dry sensors was achieved, thus demonstrating the performance of the system. Moreover, four different features of EEG signals (i.e., normal, eye-blinking, closed-eyes, and teeth-clenching signals) were measured by 16 dry sensors to ensure that they could be detected in real-life cognitive neuroscience applications. Thus, we have shown that it is possible to reliably measure EEG signals using the proposed system. This paper presents novel insights into the field of cognitive neuroscience, showing the possibility of studying brain function under real-life conditions.
A TSV-Based Bio-Signal Package With mu-Probe Array
(2014-02-01)
Chou, Lei-Chun
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Lee, Shih-Wei
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Huang, Po-Tsang
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Chang, Chih-Wei
;
Chiang, Cheng-Hao
Bio-signal probes providing stable observation with high quality signals are crucial for understanding how the brain works and how the neural signal transmits. Due to the weak and noisy characteristics of bio-signals, the connected interconnect length between the sensor and CMOS has significant impact on the bio-signal quality. In addition, long interconnections with wire bonding technique introduce noises and lead to bulky packaged systems. This letter presents an implantable through-silicon via (TSV) technology to connect sensors and CMOS devices located on the opposite sides of the chip for brain neural sensing applications. With the elimination of traditional wire bonding and packaging technologies, the quality of bio-signal can be greatly improved.
Controlling a Human-Computer Interface System With a Novel Classification Method that Uses Electrooculography Signals
(2013-08-01)
Wu, Shang-Lin
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Liao, Lun-De
;
Lu, Shao-Wei
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Jiang, Wei-Ling
;
Chen, Shi-An
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.

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🏛️國立陽明交通大學機構典藏

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