Biology subjects

Fernandez, E.

Publications and source records attributed to Fernandez, E..

4 recordsLinked to original sources

bioRxiv2019

On the Reliability of Chronically Implanted Thin-Film Electrodes in Human Arm Nerves for Neuroprosthetic Applications

Direct stimulation of peripheral nerves can successfully provide sensory feedback to amputees while using hand prostheses. Recent clinical studies have addressed this important limitation of current prostheses solutions using different implantable electrode concepts. Longevity of the electrodes is key to success. We have improved the long-term stability of the polyimide-based transverse intrafascicular multichannel electrode (TIME) that showed promising performance in clinical trials by integration of silicon carbide adhesion layers. The TIMEs were implanted in the median and ulnar nerves of three trans-radial amputees for up to six months. Here, we present the characterization of the electrical properties of the thin-film metallization as well as material status post explantationem for the first time. The TIMEs showed reliable performance in terms of eliciting sensation and stayed within the electrochemical safe limits maintaining a good working range with respect to amplitude modulation. After termination of the trials and explantation of the probes, no signs of corrosion or morphological change to the thin-film metallization was observed by means of electrochemical and optical analysis. Damage to the metallization was assigned exclusively to mechanical impacts during explantation and handling. The results indicate that thin-film metallization on polymer substrates is applicable in permanent implant system.

bioengineering↗

bioRxiv2019

Stability of Thin-Film Metallization in Flexible Stimulation Electrodes: Analysis and Improvement of in vivo Performance

Micro-fabricated neural interfaces based on polyimide (PI) are achieving increasing importance in translational research. The ability to produce well-defined micro-structures with properties that include chemical inertness, mechanical flexibility and low water uptake are key advantages for these devices. This paper reports the development of the transverse intrafascicular multichannel electrode (TIME) used to deliver intraneural sensory feedback to an upper-limb amputee in combination with a sensorized hand prosthesis. A first-in-human study limited to 30 days was performed. About 90 % of the stimulation contact sites of the TIMEs maintained electrical functionality and stability during the full implant period. However, optical analysis post-explantation revealed that 62.5 % of the stimulation contacts showed signs of mechanical damage at the metallization-PI interface. Such damage likely occurred due to handling during explantation and subsequent analysis, since a significant change in impedance was not observed in vivo. Nevertheless, whereas device integrity is mandatory for long-term functionality in chronic implantation, measures to increase the bonding strength of the metallization-PI interface deserve further investigation. We report here that silicon carbide (SiC) is an effective adhesion-promoting layer resisting heavy electrical stimulation conditions in vivo. Optical analysis of the new electrodes revealed that the metallization remained unaltered after delivering over 14 million pulses in vivo without signs of delamination at the metallization-PI interface. Reliable adhesion of thin-film metallization to substrate has been proven using SiC, improving the potential transfer of micro-fabricated neural electrodes for chronic clinical applications.

bioengineering↗

bioRxiv2019

The Psychological and Physiological Part of Emotions: Multimodal Approximation for Valence Classification

In order to develop more precise and functional affective applications, it is necessary to achieve a balance between the psychology and the engineering applied to emotions. Signals from the central and peripheral nervous systems have been used for emotion recognition purposes, however, their operation and the relationship between them remains unknown. In this context, in the present work we have tried to approach the study of the psychobiology of both systems in order to generate a computational model for the recognition of emotions in the dimension of valence. To this end, the electroencephalography (EEG) signal, electrocardiography (ECG) signal and skin temperature of 24 subjects have been studied. Each methodology has been evaluated individually, finding characteristic patterns of positive and negative emotions in each of them. After feature selection of each methodology, the results of the classification showed that, although the classification of emotions is possible at both central and peripheral levels, the multimodal approach did not improve the results obtained through the EEG alone. In addition, differences have been observed between cerebral and physiological responses in the processing emotions by separating the sample by sex; though, the differences between men and women were only notable at the physiological level.

neuroscience↗

bioRxiv2019

A new model for the implementation of positive and negative emotion recognition

The large range of potential applications, not only for patients but also for healthy people, that could be achieved by affective BCI (aBCI) makes more latent the necessity of finding a commonly accepted protocol for real-time EEG-based emotion recognition. Based on wavelet package for spectral feature extraction, attending to the nature of the EEG signal, we have specified some of the main parameters needed for the implementation of robust positive and negative emotion classification. 12 seconds has resulted as the most appropriate sliding window size; from that, a set of 20 target frequency-location variables have been proposed as the most relevant features that carry the emotional information. Lastly, QDA and KNN classifiers and population rating criterion for stimuli labeling have been suggested as the most suitable approaches for EEG-base emotion recognition. The proposed model reached a mean accuracy of 98% (s.d. 1.4) and 98.96% (s.d. 1.28) in a subject-dependent approach for QDA and KNN classifier, respectively. This new model represents a step forward towards real-time classification. Although results were not conclusive, new insights regarding subject-independent approximation have been discussed.

neuroscience↗