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Understanding of the neural response to electrical stimulation requires simultaneous recording from the various neurons of retina. Electrodes form the physical interface with the neural or retinal tissue. Successful retinal stimulation and recording demands conformal integration of these electrodes with the soft tissue to ensure establishment of proper electrical connection with the excitable tissue. Mechanical impedance of polydimethylsiloxane (PDMS) being compliant with that of retinal tissue, offers excellent potential as a substrate for metal electrodes. In this paper, Cr/Au micro electrodes with 200 μm diameter were fabricated on rigid and flexible PDMS substrates under crack free condition. Spontaneous buckling of thin films over PDMS substrates improved electrode performance circumventing the fabrication issues faced over a buckled surface. Individual electrodes from the multielectrode arrays (MEAs) were examined with electrochemical impedance spectroscopy and cyclic voltammetry. Controlled fabrication process as described here generates buckles in the metal films leading to increased electrode surface area that increases the charge storage capacity and decreases the interface impedance of the metal electrodes. At 1 kHz, impedance was reduced from 490 ± 27 kΩ to 246 ± 19 kΩ and charge storage capacity was increased from 0.40 ± 0.87 mC/cm to 2.1 ± 0.87 mC/cm. Neural spikes recorded with PDMS based electrodes from isolated retina also contained less noise as indicated by signal to noise ratio analysis. The present study established that the use of PDMS as a substrate for MEAs can enhance the performance of any thin film metal electrodes without incorporation of any coating layers or nanomaterials.

A stable grasp is attained through appropriate hand preshaping and precise fingertip forces. Here, we have proposed a method to decode grasp patterns from motor imagery and subsequent fingertip force estimation model with a slippage avoidance strategy. We have developed a feature-based classification of electroencephalography (EEG) associated with imagination of the grasping postures. Chaotic behaviour of EEG for different grasping patterns has been utilised to capture the dynamics of associated motor activities. We have computed correlation dimension (CD) as the feature and classified with "one against one" multiclass support vector machine (SVM) to discriminate between different grasping patterns. The result of the analysis showed varying classification accuracies at different subband levels. Broad categories of grasping patterns, namely, power grasp and precision grasp, were classified at a 96.0% accuracy rate in the alpha subband. Furthermore, power grasp subtypes were classified with an accuracy of 97.2% in the upper beta subband, whereas precision grasp subtypes showed relatively lower 75.0% accuracy in the alpha subband. Following assessment of fingertip force distributions while grasping, a nonlinear autoregressive (NAR) model with proper prediction of fingertip forces was proposed for each grasp pattern. A slippage detection strategy has been incorporated with automatic recalibration of the regripping force. Intention of each grasp pattern associated with corresponding fingertip force model was virtualised in this work. This integrated system can be utilised as the control strategy for prosthetic hand in the future. The model to virtualise motor imagery based fingertip force prediction with inherent slippage correction for different grasp types ᅟ.