Bypassing the Natural Visual-Motor Pathway to Execute Complex Movement Related Tasks Using Interval Type-2 Fuzzy Sets

In visual-motor coordination, the human brain processes visual stimuli representative of complex motion-related tasks at the occipital lobe to generate the necessary neuronal signals for the parietal and pre-frontal lobes, which in turn generates movement related plans to excite the motor cortex to execute the actual tasks. The paper introduces a novel approach to provide rehabilitative support to patients suffering from neurological damage in their pre-frontal, parietal and/or motor cortex regions. An attempt to bypass the natural visual-motor pathway is undertaken using interval type-2 fuzzy sets to generate the approximate EEG response of the damaged pre-frontal/parietal/motor cortex from the occipital EEG signals. The approximate EEG response is used to trigger a pre-trained joint coordinate generator to obtain desired joint coordinates of the link end-points of a robot imitating the human subject. The robot arm is here employed as a rehabilitative aid in order to move each link end-points to the desired locations in the reference coordinate system by appropriately activating its links using the well-known inverse kinematics approach. The mean-square positional errors obtained for each link end-points is found within acceptable limits for all experimental subjects including subjects with partial parietal damage, indicating a possible impact of the proposed approach in rehabilitative robotics. Subjective variation in EEG features over different sessions of experimental trials is modelled here using interval type-2 fuzzy sets for its inherent power to handle uncertainty. Experiments undertaken confirm that interval type-2 fuzzy realization outperforms its classical type-1 counterpart and back-propagation neural approaches in all experimental cases, considering link positional error as a metric. The proposed research offers a new opening for the development of possible rehabilitative aids for people with partial impairment in visual-motor coordination

Analysing Vibrotactually Stimulated EEG Signals to Comprehend Object Shapes

Tactile feedback has the capability of reducing the workload on the visual channel, during visual feedback in brain-computer interfaces (BCIs). It is requisite to analyse the brain signals corresponding to the tactile stimulations. This work is aimed at analysing the brain signals while the users are vibrotactually stimulated. The brain signals are acquired non-invasively by electroencephalography (EEG), while brushless coin-type vibration motors are actuated in particular patterns to convey the object shape information on subjects' skin surface in form of vibrations. The acquired EEG signals are pre-processed to eliminate the effect of various types of noises and to extract the EEG signals corresponding to relevant frequency bands. Adaptive autoregressive (AAR) parameters are extracted from the pre-processed EEG signals and are finally classified by Naive Bayesian (NB) approach, in order to recognize the vibratotactually stimulated object shapes from brain signals. In addition to the classifier output, subjects' verbal responses about the object shape they perceived are also noted for validation. Three successive sessions of shape recognition from vibrotactile pattern show an improvement in EEG classification accuracy from 63.75% to 74.37%, and also depicted learning of the stimulus from subjects' psychological response which is observed to increase from 75% to 95%. This observation substantiates the learning of vibrotactile stimulation in user over the sessions which in turn increases the system efficacy

Performance Analysis of Object Shape Classification and Matching from Tactile Images Using Wavelet Energy Features

AbstractTactile images while grasping objects are acquired and wavelet based features are extracted for matching and classification. The performance of matching and classification is evaluated in terms of matching rate and classification accuracy along with the computation times. This comparison will help in determining the applicability of classification or matching in future works including real time applications. Highest classification accuracy is found to be 86%, in 0.0619 sec, while the best matching r ate obtained is 96% in 0.0041 sec. Thus Image matching is suitable for real time applications taking less computation time while providing significant performance improvement at the same time