S. Arjunan

We present in this paper a study of three EEG signals feature extraction techniques. Expand

This paper reports the use of fractal properties of sEMG to reliably identify individual wrist and finger flexion, overcoming the earlier shortcomings. Expand

This paper describes a voiceless speech recognition technique that utilizes dynamic visual features to represent the facial movements during phonation. Expand

We have investigated the group-difference in the dynamic features during sketching of spiral between PD and control subjects with the aim of developing an accurate method for diagnosing PD patients. Expand

This paper describes the analysis of the variance of the amplitude of surface electromyogram (SEMG) recorded from the L4/ L5 region of the erector spinae for healthy participants and people suffering with low back pain (LBP) when they were walking and running on a treadmill. Expand

This paper describes a new technique for recognizing speech using visual speech information. Expand

Pattern classification of Myo-Electrical signal during different Maximum Voluntary Contractions: A study using BSS techniques using Independent Component Analysis (ICA). Expand

This study has developed a technique for identifying the presence of muscle fatigue based on spatial changes of the normalised mutual information (NMI) between multiple high density surface electromyography (HD-sEMG) channels. Expand