Nano-fractal Electrodes Use in Electroencephalography (EEG) Array Generation to Map with Speech Signals

In the present study, the adhesion of TiO2 nanotubes (TiO2-NTs) to thicker substrates was improved by decorating them with metallic Ag nanoparticles (NPs) using the photo-reduction process. The obtained coatings were characterized using X-ray diffraction (XRD), scanning electron microscopy (SEM) combined with energy-dispersive X-ray spectroscopy (EDXS) and transmission electron microscope (TEM). X-ray diffraction (XRD) confirmed that all TiO2-NTs crystallized in anatase after annealing at 400°C regardless of the anodization potential. The scanning electron microscopy (SEM) indicated that the TiO2-NTs were uniformly distributed on the substrate with an ordered and vertically aligned morphology. It also revealed that the diameter of the TiO2-NTs reached~100 nm. The decoration of TiO2-NTs surface with silver nanoparticles was assessed by transmission electron microscopy (TEM). Moreover, a new scratch test mode called ‘wear mode’ was performed to evaluate the wear resistance of the coatings. Results obtained by the scratch tests proved that the decorated coating with Ag nanoparticles improved the interfacial adhesion. The friction coefficient decreased from 0.65 to 0.45 when pure TiO2 was decorated with 10 min-Ag-NPs.The wear behavior was studied using a multi-pass scratch test. It was found that the wear volume reduced with the incorporation of Ag nanoparticles. The study of the damage mechanisms showed visco-elastic plastic deformation in the pure TiO2 coating.

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Classification Algorithms for EEG-Based Brain-Computer Interface

Ramakuri

Chakraboirty

Peddi

et al. 2019

Advances in Medical Technologies and Clinical Practice

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In recent years, a vast research is concentrated towards the development of electroencephalography (EEG)-based human-computer interface in order to enhance the quality of life for medical as well as nonmedical applications. The EEG is an important measurement of brain activity and has great potential in helping in the diagnosis and treatment of mental and brain neuro-degenerative diseases and abnormalities. In this chapter, the authors discuss the classification of EEG signals as a key issue in biomedical research for identification and evaluation of the brain activity. Identification of various types of EEG signals is a complicated problem, requiring the analysis of large sets of EEG data. Representative features from a large dataset play an important role in classifying EEG signals in the field of biomedical signal processing. So, to reduce the above problem, this research uses three methods to classify through feature extraction and classification schemes.

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Nano-fractal Electrodes Use in Electroencephalography (EEG) Array Generation to Map with Speech Signals

Cited by 3 publications

References 4 publications

Fabrication of chitosan/Au-TiO2 nanotube-based dry electrodes for electroencephalography recording

Fabrication of chitosan/Au-TiO2 nanotube-based dry electrodes for electroencephalography recording

Enhanced Interfacial Adhesion of TiO2 Nanotubes Decorated With Ag Silver Nanoparticles Prepared by Photo-Reduction Process

Classification Algorithms for EEG-Based Brain-Computer Interface

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