BioTech & Human Enhancement Theory/Ethics

Assistive Communication For people with locked-in syndrome – the inability to move and to speak despite being fully...

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IEEE Xplore, delivering full text access to the world's highest quality technical literature in engineering and technology. | IEEE Xplore

IEEE Xplore, delivering full text access to the world's highest quality technical literature in engineering and technology. | IEEE Xplore

IEEE Xplore, delivering full text access to the world's highest quality technical literature in engineering and technology. | IEEE Xplore

IEEE Xplore, delivering full text access to the world's highest quality technical literature in engineering and technology. | IEEE Xplore

IEEE Xplore, delivering full text access to the world's highest quality technical literature in engineering and technology. | IEEE Xplore

IEEE Xplore, delivering full text access to the world's highest quality technical literature in engineering and technology. | IEEE Xplore

IEEE Xplore, delivering full text access to the world's highest quality technical literature in engineering and technology. | IEEE Xplore

IEEE Xplore, delivering full text access to the world's highest quality technical literature in engineering and technology. | IEEE Xplore

IEEE Xplore, delivering full text access to the world's highest quality technical literature in engineering and technology. | IEEE Xplore

IEEE Xplore, delivering full text access to the world's highest quality technical literature in engineering and technology. | IEEE Xplore

IEEE Xplore, delivering full text access to the world's highest quality technical literature in engineering and technology. | IEEE Xplore

This paper presents the design and development of BioLogger, a wireless physiological signal sensing and logging system. BioLogger can simultaneously monitor and record various types of physiological signals. Energy saving design is incorporated to both hardware and software design phases in order to prolong the life time of sensor nodes. Moreover, a simple scheduler is implemented to make sure the emergency would not be missed.

This article presents the implementation of an Electroencephalogram - EEG system less susceptible to electromagnetic interference - EMI. The amplification, filtering and A/D conversion are placed directly over each electrode. Due to the local amplification and conversion, the system becomes more resilient to EMI and consequently the results are trustier. It was developed a central chip or hub that collects data of all electrodes and send it wireless to an external computer or a registering device. Since the amplification and filtering are adjustable, the hub also receives the amplification/filtering parameters from the computer and sends them to the circuits over the electrodes. The amplification/filtering adjustment improves the signal quality since electrode is properly adjusted. This article describes the protocol and circuitry of the bidirectional communication between the hub and the electrodes. The circuit was developed in Verilog and validated on a set of FPGAs, where one FPGA works as a hub and the others work as electrodes. The results show the system works properly. The amplification/filtering is individually adjusted, thus providing great flexibility to the system.

The universal serial bus (USB) has become the most popular communication interface among personal computers and embedded devices because of its ease of use, low cost, data bandwidth and availability in most computing systems. Microchippsilas PIC 18Fx550 microcontroller has an embedded USB controller that allows rapid development of USB enabled devices. This paper presents a system for bidirectional communication between a personal computer and an embedded device using a PIC 18F2550 microcontroller in USB bulk transfer mode. A comparison between three different drivers for host application was done to determine the advantages of each one. Finally, a case of study for a remote home lighting control system is presented.

The use of code compression in embedded systems based on standard RISC instruction set architectures (ISA) has been shown in the past to be of benefit in reducing overall system cost. The 16-bit THUMB ISA from ARM Ltd has a significantly higher density than the original 32-bits ARM ISA. In this paper we propose a new memory compression architecture, which employs a lossless data compression algorithm to achieve a further size reduction of around 20% on the THUMB code. We show that in some applications, the decompression can be performed in software on the main system processor without excessive processing time overheads.

The intelligent recognition of epileptic electro-encephalogram (EEG) signals is a valuable tool for the epileptic seizure detection. Recent deep learning models fail to fully consider both spectral and temporal domain representations simultaneously, which may lead to omitting the nonstationary or nonlinear property in epileptic EEGs and further produce a suboptimal recognition performance consequently. In this paper, an end-to-end EEG seizure detection framework is proposed by using a novel channel-embedding spectral-temporal squeeze-and-excitation network (CE-stSENet) with a maximum mean discrepancy-based information maximizing loss. Specifically, the CE-stSENet firstly integrates both multi-level spectral and multi-scale temporal analysis simultaneously. Hierarchical multi-domain representations are then captured in a unified manner with a variant of squeeze-and-excitation block. The classification net is finally implemented for epileptic EEG recognition based on features extracted in previous subnetworks. Particularly, to address the fact that the scarcity of seizure events results in finite data distribution and the severe overfitting problem in seizure detection, the CE-stSENet is coordinated with a maximum mean discrepancy-based information maximizing loss for mitigating the overfitting problem. Competitive experimental results on three EEG datasets against the state-of-the-art methods demonstrate the effectiveness of the proposed framework in recognizing epileptic EEGs, indicating its powerful capability in the automatic seizure detection.