1,720,984 research outputs found
A low power wireless and wearable 8-channels EEG recording system
No full textThe electroencephalography (EEG) is a technique commonly used for detecting symptoms of
neurological diseases such as epilepsy, sleep disorders, anxiety and learning disabilities that are
quite diffused and with a great impact on people common life. Most of the mentioned mental
disorders require long term EEG monitoring, possibly during daily life activities, to follow the
course of the disease and sometimes to prevent further degradations of the patient condition.
Generally, the longer the EEG measurement period isthe higher is the probability of a successful
event detection. Allowing only few hours of observation time with high costs and resource
overheads, traditional inpatient ambulatory EEG systems don’t satisfy these requirements. It is only
recently that technology innovations have led to new outpatient EEG systems. They are mobile
solutions that overcome some of these limitations reducing the overall patient monitoring costs and
increasing the effectiveness of the measurements [1]. Despite their benefits, such systems are still
cumbersome with some problems related to the electrode-skin adherence, to the data storage
capability and to the battery life time. Wearable EEG such as those presented in [2] and [3], are
aimed to overcome these issues, allowing the recording of a longer temporal window that includes
all stages of sleep and wakefulness and increasing the likelihood of recording typical symptoms.
The wearable EEG system proposed in this paper is based on a custom PCB with off-the-shelf
components. Being a wearable device, special efforts were made in reducing its power consumption
and in device miniaturization. As a result, only the essential components were included in the
project: an amplyfing/filtering block, an analog todigital converter, a micro-controller, a bluetooth
transceiver and a power management module. The designed system, depicted in Fig. 1, contains a
differential 8 channel recording unit. The EEG signals detected with a standard EEG cap are first
amplified and then converted into a 24-bit digital signals by an ADS1299 from Texas Instrument.
Once acquired, digital signals are transmitted to a remote backend by means of a Microchip
Bluetooth RN-42 module. Moreover, a USB connection was introduced to charge the EEG recorder
battery and as additional channel for data transfer. A custom firmware was written for a Microchip
PIC18F46J50 to coordinate data exchange between ADCand Bluetooth or USB external controller.
The main constraint was the real time data exchange at sampling frequency up to 2000SPS. In
addition a power management unit generates all digital and analog voltage supplies from a 3.7V-950mAh LiPo battery. Even the battery charging circuit was implemented on the board. The EEG
recorder was realized on the 5.5cm X 3.5cm double face board depicted in Fig.2. Possible remote
controllers for wearable EEG recording applicationsare the nowadays widely diffused smartphone
or tablet. So that an open source and user-friendly software application based, for example, on
Android operative system, can be a target solution to interface our EEG recorder. At this first stage
of the system development a Visual C++ application was written for the EEG recorder testing
purpose. The small dimensions of the realized system and its maximum 270mW of power
consumption make it suitable for up to 13 hours of continuous EEG recording without encumbering
any daily life activity. As depicted in Fig. 3 and Fig. 4, some in-vivo measurements were performed
comparing our device with a standard laboratory equipment
Stationary Wavelet Processing and Data Imputing in Myoelectric Pattern Recognition on a Low-Cost Embedded System
Get PDFPattern recognition-based decoding of surface electromyography allows for intuitive and flexible control of prostheses but comes at the cost of sensitivity to in-band noise and sensor faults. System robustness can be improved with wavelet-based signal processing and data imputing, but no attempt has been made to implement such algorithms on real-time, portable systems. The aim of this work was to investigate the feasibility of low-latency, wavelet-based processing and data imputing on an embedded device capable of controlling upper-arm prostheses. Nine able-bodied subjects performed Motion Tests while inducing transient disturbances. Additional investigation was performed on pre-recorded Motion Tests from 15 able-bodied subjects with simulated disturbances. Results from real-time tests were inconclusive, likely due to the low number of disturbance episodes, but simulated tests showed significant improvements in most metrics for both algorithms. However, both algorithms also showed reduced responsiveness during disturbance episodes. These results suggest wavelet-based processing and data imputing can be implemented in portable, real-time systems to potentially improve robustness to signal distortion in prosthetic devices with the caveat of reduced responsiveness for the typically short duration of signal disturbances. The trade-off between large-scale signal corruption robustness and system responsiveness warrants further studies in daily life activities
Evaluation of computer-based target achievement tests for myoelectric control
Get PDFReal-Time evaluation of novel prosthetic control schemes is critical for translational research on artificial limbs. Recently, two computer-based, real-Time evaluation tools, the target achievement control (TAC) test and the Fitts' law test (FLT), have been proposed to assess real-Time controllability. Whereas TAC tests provides an anthropomorphic visual representation of the limb at the cost of confusing visual feedback, FLT clarifies the current and target locations by simplified non-Anthropomorphic representations. Here, we investigated these two approaches and quantified differences in common performance metrics that can result from the chosen method of visual feedback. Ten able-bodied and one amputee subject performed target achievement tasks corresponding to the FLT and TAC test with equivalent indices of difficulty. Ablebodied subjects exhibited significantly (p <0.05) better completion rate, path efficiency, and overshoot when performing the FLT, although no significant difference was seen in throughput performance. The amputee subject showed significantly better performance in overshoot at the FLT, but showed no significant difference in completion rate, path efficiency, and throughput. Results from the FLT showed a strong linear relationship between the movement time and the index of difficulty (R2 D 0:96), whereas TAC test results showed no apparent linear relationship (R2 D 0:19). These results suggest that in relatively similar conditions, the confusing location of virtual limb representation used in the TAC test contributed to poorer performance. Establishing an understanding of the biases of various evaluation protocols is critical to the translation of research into clinical practice
A wearable device for high-frequency EEG signal recording
No full textThe recording of high-frequency oscillations (HFO) through the skull has been investigated in the last years highlighting interesting new correlations between the EEG signals and common mental diseases. Therefore, since most of the commercially available EEG acquisition systems are focused on the low frequency signals, a wide-band EEG recorder is here presented. The proposed system is designed for those applications in which a wearable and user-friendly device is required. Using a standard Bluetooth (BT) module to transfers the acquired signals to a remote back-end, it can be easily interfaced with the nowadays widely spread smartphones or tablets by means of a mobile-based application. A Component Off-The-Shelf (COTS) device was designed on a 19 cm2 custom PCB with a low-power 8-channel acquisition module and a 24−bit Analog to Digital Converter (ADC). The presented system, validated through in-vivo experiments, allows EEG signals recording at different sample rates, with a maximum bandwidth of 524 Hz, and exhibits a maximum power consumption of 270 mW
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Myoelectric signals and pattern recognition from implanted electrodes in two TMR subjects with an osseointegrated communication interface
No full textPermanent implantation of electrodes for prosthetic control is now possible using an osseointegrated implant as a long-term stable communication interface (e-OPRA). The number of myoelectric sites to host such electrodes can be increased by Targeted Muscle Reinnervation (TMR). Traditionally, patients need to wait several months before the TMR signals are strong enough to be recorded by electrodes placed over the skin. In this study, we report the evolution of the TMR myoelectric signals recorded from two subjects via implanted electrodes using e-OPRA, and monitored for up to 48 weeks after surgery. The signals were analyzed with regard to amplitude (signal-to-noise ratio), independence (cross-correlation) and myoelectric pattern recognition (classification accuracy). TMR signals appeared at the first follow-up, one month post-surgery, and developed around 20 dB by the last. Cross-correlation between signals decreased over time and converged to a few percentage points. Classification accuracies were over 97% by the last follow up. These preliminary results suggest that implanted electrodes via the e-OPRA interface allow for an earlier and more effective use of motor signals from TMR sites compared to conventional skin surface electrodes
Chronic Use of a Sensitized Bionic Hand Does Not Remap the Sense of Touch
Get PDFElectrical stimulation of tactile nerve fibers that innervated an amputated hand results in vivid sensations experienced at a specific location on the phantom hand, a phenomenon that can be leveraged to convey tactile feedback through bionic hands. Ideally, electrically evoked sensations would be experienced on the appropriate part of the hand: touch with the bionic index fingertip, for example, would elicit a sensation experienced on the index fingertip. However, the perceived locations of sensations are determined by the idiosyncratic position of the stimulating electrode in the nerve and thus are difficult to predict or control. This problem could be circumvented if perceived sensations shifted over time to become consistent with the position of the sensor that triggers them. We show that, after long-term use of a neuromusculoskeletal prosthesis that featured a mismatch between the sensor location and the resulting tactile experience, the perceived location of the touch did not change
Cross-Channel Impedance Measurement for Monitoring Implanted Electrodes
No full textImplanted electrodes, such as those used for cochlear implants, brain-computer interfaces, and prosthetic limbs, rely on particular electrical conditions for optimal operation. Measurements of electrical impedance can be a diagnostic tool to monitor implanted electrodes for changing conditions arising from glial scarring, encapsulation, and shorted or broken wires. Such measurements provide information about the electrical impedance between a single electrode and its electrical reference, but offer no insights into the overall network of impedances between electrodes. Other solutions generally rely on geometrical assumptions of the arrangement of the electrodes and may not generalize to other electrode networks. Here, we propose a linear algebra-based approach, Cross-Channel Impedance Measurement (CCIM), for measuring a network of impedances between electrodes which all share a common electrical reference. This is accomplished by measuring the voltage response from all electrodes to a known current applied between each electrode and the shared reference, and is agnostic to the number and arrangement of electrodes. The approach is validated using a simulated 8-electrode network, demonstrating direct impedance measurements between electrodes and the reference with 96.6% ±0.2% accuracy, and cross-channel impedance measurements with 93.3% ±0.6% accuracy in a typical system. Subsequent analyses on randomized systems demonstrate the sensitivity of the model to impedance range and measurement noise. Clinical Relevance- CCIM provides a system-agnostic diagnostic test for implanted electrode networks, which may aid in the longitudinal tracking of electrode performance and early identification of electronics failures
Explorations of Autonomous Prosthetic Grasping Via Proximity Vision and Deep Learning
Get PDFThe traumatic loss of a hand is usually followed by significant psychological, functional and rehabilitation challenges. Even though much progress has been reached in the past decades, the prosthetic challenge of restoring the human hand functionality is still far from being achieved. Autonomous prosthetic hands showed promising results and wide potential benefit, a benefit that must be still explored and deployed. Here, we hypothesized that a combination of a radar sensor and a low-resolution time-of-flight camera can be sufficient for object recognition in both static and dynamic scenarios. To test this hypothesis, we analyzed via deep learning algorithms HANDdata, a human-object interaction dataset with particular focus on reach-to-grasp actions. Inference testing was also performed on unseen data purposely acquired. The analyses reported here, broken down to gradually increasing levels of complexity, showed a great potential of using such proximity sensors as alternative or complementary solution to standard camera-based systems. In particular, integrated and low-power radar can be a potential key technology for next generation intelligent and autonomous prostheses
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