Steady state visual evoked potential (SSVEP)-based brain-computer interface (BCI) has gained a lot of attention due to its robustness and high information transfer rate (ITR). However, transitioning well-controlled laboratory-oriented BCI demonstrations to real-world applications poses severe challenges for this exciting field. For instance, conducting BCI experiments usually requires skilled technicians to abrade the area of skin underneath each electrode and apply an electrolytic gel or paste to acquire high-quality SSVEPs from hair-covered areas. Our previous proof-of-concept study has proposed an alternative approach that employed electroencephalographic signals collected from easily accessible non-hair-bearing areas including neck, behind the ears, and face to realize an SSVEP-based BCI. The study results showed that, with proper electrode placements and advanced signal-processing algorithms, the SSVEPs measured from non-hair-bearing areas in off-line SSVEP experiments could achieve comparable SNR to that obtained from the hair-bearing occipital areas. This study extended the previous work to systematically investigate the costs and benefits of non-hair SSVEPs. Furthermore, this study developed and evaluated an online BCI system based solely on non-hair EEG signals. A 12-target identification task was employed to quantitatively assess the performance of the online SSVEP-based BCI system. All subjects successfully completed the tasks using non-hair SSVEPs with 84.08 ± 15.60% averaged accuracy and 30.21 ± 10.61 bits/min averaged ITR. The empirical results of this study demonstrated the practicality of implementing an SSVEP-based BCI based on signals from non-hair-bearing areas, significantly improving the feasibility and practicality of real-world BCIs.
In America, 60% of adults reported that they have driven a motor vehicle while feeling drowsy, and at least 15–20% of fatal car accidents are fatigue-related. This study translates previous laboratory-oriented neurophysiological research to design, develop, and test an On-line Closed-loop Lapse Detection and Mitigation (OCLDM) System featuring a mobile wireless dry-sensor EEG headgear and a cell-phone based real-time EEG processing platform. Eleven subjects participated in an event-related lane-keeping task, in which they were instructed to manipulate a randomly deviated, fixed-speed cruising car on a 4-lane highway. This was simulated in a 1st person view with an 8-screen and 8-projector immersive virtual-reality environment. When the subjects experienced lapses or failed to respond to events during the experiment, auditory warning was delivered to rectify the performance decrements. However, the arousing auditory signals were not always effective. The EEG spectra exhibited statistically significant differences between effective and ineffective arousing signals, suggesting that EEG spectra could be used as a countermeasure of the efficacy of arousing signals. In this on-line pilot study, the proposed OCLDM System was able to continuously detect EEG signatures of fatigue, deliver arousing warning to subjects suffering momentary cognitive lapses, and assess the efficacy of the warning in near real-time to rectify cognitive lapses. The on-line testing results of the OCLDM System validated the efficacy of the arousing signals in improving subjects' response times to the subsequent lane-departure events. This study may lead to a practical on-line lapse detection and mitigation system in real-world environments.
We propose an improved algorithm for highperformance substrate verification utilizing multi-probe testers. Previous works[3, 91 only consider some minimum test sets with complete fault coverage to optimize probe routes. Since there exist numerous such sets for a design, our algorithm dynamically selects preferred ones using a linear validation routine during the probe route optimization phase. By considering the test generation and the probe scheduling simultaneously, the algorithm has demonstrated its superiority over previous works.
There is considerable interest in the development of optical interconnects for multichip modules (MCM's) to improve their performance. For effective utilization of the optical and electronic technologies, a methodology for partitioning the system is required. The key question to be answered is which technology should be used for each interconnect in a given netlist: optical or electronic. We introduce the computer-aided design approach for partitioning optoelectronic systems into optoelectronic MCM's. We first discuss the design trade-off issues in an optoelectronic system design, including speed, power dissipation, area, and diffraction limits for free-space optics. We then define a formulation for optoelectronic MCM partitioning and describe new algorithms for optimizing this partitioning based on the minimization of the power dissipation. The models for the algorithms are discussed in detail, and an example of a multistage interconnect network is given. Different results, with the number and size of chips being variable, are presented in which improvement for the system packaging has been observed when the partitioning algorithms are applied.
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