M. Ramasubba Reddy

Bio: M. Ramasubba Reddy is an academic researcher from Indian Institute of Technology Madras. The author has contributed to research in topics: Imaging phantom & Speckle pattern. The author has an hindex of 11, co-authored 71 publications receiving 636 citations. Previous affiliations of M. Ramasubba Reddy include Indian Institutes of Technology.

Designing a Sum of Squared Correlations Framework for Enhancing SSVEP-Based BCIs

Abstract: This study illustrates and evaluates a novel subject-specific target detection framework, sum of squared correlations (SSCOR), for improving the performance of steady-state visual evoked potential (SSVEP) based brain-computer interfaces (BCIs). The SSCOR spatial filter learns a common SSVEP representation space through the optimization of the individual SSVEP templates. The projection onto this SSVEP response subspace improves the signal to noise ratio (SNR) of the SSVEP components embedded in the recorded electroencephalographic (EEG) data. To demonstrate the effectiveness of the proposed framework, the target detection performance of the SSCOR method is compared with the state of the art task-related component analysis (TRCA). The evaluation is conducted on a 40 target SSVEP benchmark data collected from 35 subjects. The results of the extensive comparisons of the performance metrics show that the proposed SSCOR method outperforms the TRCA method. The ensemble version of the SSCOR framework provides an offline simulated information transfer rate (ITR) of 387 ± 9 bits/min which is much higher than that of the ensemble TRCA approach (max. ITR 216 ± 27 bits/min). The significant improvement in the detection accuracy and simulated ITR demonstrates the efficacy of the proposed framework for target detection in SSVEP based BCI applications.

EOG based virtual keyboard

Abstract: In this paper, an EOG based assistive system for typing text using a virtual keyboard is presented. An indigenously developed acquisition system based on arduino interfaced ADS1299 with a wearable dry electrode mask is used to record and process EOG signals. An accuracy of 100% and an average speed of 1 char/12 sec was achieved by an untrained person in online implementation of this system. This can be further improvised by word prediction algorithms

The analysis on band-limited hypernasal speech using group delay based formant extraction technique

Abstract: Speakers with defective velopharyngealmechanism, produce speech with inappropriate nasal resonances across vowelsounds. Theacousticanalysisonhypernasalspeech andnasalized vowelsofnormalspeechshowsthatthereis an additional frequency introduced in the low frequency region close to the first formant frequency [1]. The conventional formant extraction techniques may fail to resolve closely spaced formants. In this paper, an attempt is made to use the groupdelay based algorithm [2] for the extractionofformantfrequenciesfromhypernasalspeech. Preliminary experiments on synthetic signal with closely spacedformantsshowthattheformantsarebetterresolved in group delay spectrum when compared to conventional methods. But when formants are too close with wider bandwidths, the group delay algorithm also fails to resolve prominently. This is primarily because of the influence of the other resonances in the signal. To extract the additional frequencyclose to the first formant, the speech signal is low-pass filtered and the formants are extracted using group delay function. Following the satisfactory results on synthetic signal, the above technique is used to extract formants from phonations /a/, /i/, and /u/ uttered by 15 speakers with cleft palate who are expected to produce hypernasal speech. Invariably in all the tests, an additional nasal resonance around 250 Hz and first formant frequency of vowels are resolved properly.

Non invasive blood flow assessment in diabetic foot ulcer using laser speckle contrast imaging technique

Abstract: Measuring microcirculatory tissue blood perfusion is of interest for both clinicians and researchers in a wide range of applications and can provide essential information of the progress of treatment of certain diseases which causes either an increased or decreased blood flow. Diabetic ulcer associated with alterations in tissue blood flow is the most common cause of non-traumatic lower extremity amputations. A technique which can detect the onset of ulcer and provide essential information on the progress of the treatment of ulcer would be of great help to the clinicians. A noninvasive, noncontact and whole field laser speckle contrast imaging (LSCI) technique has been described in this paper which is used to assess the changes in blood flow in diabetic ulcer affected areas of the foot. The blood flow assessment at the wound site can provide critical information on the efficiency and progress of the treatment given to the diabetic ulcer subjects. The technique may also potentially fulfill a significant need in diabetic foot ulcer screening and management.

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

A survey of stimulation methods used in SSVEP-based BCIs

Abstract: Brain-computer interface (BCI) systems based on the steady-state visual evoked potential (SSVEP) provide higher information throughput and require shorter training than BCI systems using other brain signals. To elicit an SSVEP, a repetitive visual stimulus (RVS) has to be presented to the user. The RVS can be rendered on a computer screen by alternating graphical patterns, or with external light sources able to emit modulated light. The properties of an RVS (e.g., frequency, color) depend on the rendering device and influence the SSVEP characteristics. This affects the BCI information throughput and the levels of user safety and comfort. Literature on SSVEP-based BCIs does not generally provide reasons for the selection of the used rendering devices or RVS properties. In this paper, we review the literature on SSVEP-based BCIs and comprehensively report on the different RVS choices in terms of rendering devices, properties, and their potential influence on BCI performance, user safety and comfort.

Multiple Channel Detection of Steady-State Visual Evoked Potentials for Brain-Computer Interfaces

Abstract: In this paper, novel methods for detecting steady-state visual evoked potentials using multiple electroencephalogram (EEG) signals are presented. The methods are tailored for brain-computer interfacing, where fast and accurate detection is of vital importance for achieving high information transfer rates. High detection accuracy using short time segments is obtained by finding combinations of electrode signals that cancel strong interference signals in the EEG data. Data from a test group consisting of 10 subjects are used to evaluate the new methods and to compare them to standard techniques. Using 1-s signal segments, six different visual stimulation frequencies could be discriminated with an average classification accuracy of 84%. An additional advantage of the presented methodology is that it is fully online, i.e., no calibration data for noise estimation, feature extraction, or electrode selection is needed