Shovan Barma

Indian Institute of Information Technology Guwahati | IIITG · Department of Electronics and Communication Engineering (ECE)

Focal cortical dysplasia type II (FCD-II) is a prominent cortical development malformation associated with drug-resistant epileptic seizures that leads to lifelong cognitive impairment. Efficient MRI, followed by its analysis (e.g., cortical abnormality distinction, precise localization assistance, etc.) plays a crucial role in the diagnosis and su...

Virtual staining of microscopy specimens using GAN-based methods could resolve critical concerns of manual staining process as displayed in recent studies on histopathology images. However, most of these works use basic-GAN framework ignoring microscopy image characteristics and their performance were evaluated based on structural and error statist...

This work details design and development of a microscopy image-based vegetable quality assessment system (Prototype) by adopting deep learning (DL) technique on edge device. Current automated machine learning methods primarily utilize outer-surface images of vegetables/fruits, often lacking in precise quantification of nutrient content such as carb...

In recent advancements of lightweight deep architectures for edge devices, most of the works follow typical MobileNet pipeline designed for computer vision tasks which is not very appropriate for microscopy image analysis. Certainly, design of dedicated lightweight network for highly complex microscopy image analysis has not been attempted so far....

This study proposes a multi-class classification technique based on Multifractal Spectra for different types of cardiac arrhythmias which are associated with irregularity and/or complex dynamics of the heart. Indeed, the degree of complexity of such dynamics is diverse for different states of cardiac condition. Certainly, such physiological respons...

Emotion can be influenced during self-isolation, and to avoid severe mood swings, emotional regulation is meaningful. To achieve this, efficiently recognizing emotion is a vital step, which can be realized by electroencephalography signals. Previously, inspired by the knowledge of sequencing in bioinformatics, a method termed brain rhythm sequencin...

Electroencephalogram (EEG) based seizure types classification has not been addressed well, compared to seizure detection, which is very important for the diagnosis and prognosis of epileptic patients. The minuscule changes reflected in EEG signals among different seizure types make such tasks more challenging. Therefore, in this work, underlying fe...

Recently, electroencephalography (EEG) signals have shown great potential for emotion recognition. Nevertheless, multichannel EEG recordings lead to redundant data, computational burden, and hardware complexity. Hence, efficient channel selection, especially single-channel selection, is vital. For this purpose, a technique termed brain rhythm seque...

In EEG-based emotion recognition, subject’s affective responses are captured by several channels (scalp level) by presenting target stimuli. However, the emotional responses are inconsistent throughout the acquired signal, and rather, it arises at certain duration with high prominence on certain channels. However, existing studies ignored this vita...

This work proposes a technique that analyzes electroencephalography (EEG) using brain rhythms ( $\delta $ , $\theta $ , $\alpha $ , $\beta $ , and $\gamma $ ) presented in a sequential format and applies it for emotion recognition. Although brain rhythms are regarded as reliable parameters in EEG-based emotion recognition, to achieve high...

Discrimination of types of seizure using the Electroencephalogram (EEG) signal has always been a challenging task due to minuscule differences among different types of seizures. In this regard, deep learning (DL) which has already evidenced notable performance in image recognition could be suitable. However, a few attempts have been made so far in...

The similarity is a fundamental measure from the homology theory in bioinformatics, and the biological sequence can be classified based on it. However, such an approach has not been utilized for electroencephalography (EEG)-based emotion recognition. To this end, the sequence generated by choosing the dominant brain rhythm owning maximum instantane...

classification of seizure types plays a crucial role in diagnosis and prognosis of epileptic patients which has not been addressed properly, while most of the works are surrounded by seizure detection only. However, in recent times, few works have been attempted on the classification of seizure types using deep learning (DL). In this work, a novel...

A convolution neural network (CNN) architecture has been designed to classify epileptic seizures based on two-dimensional (2D) images constructed from decomposed mono-components of electroencephalogram (EEG) signals. For the decomposition of EEG, Hilbert vibration decomposition (HVD) has been employed. In this work, four brain rhythms - delta, thet...

This work proposes an emotion recognition technique by distinguishing appropriate electroencephalogram (EEG) segments from acquired signal for target emotions. Generally, EEG based emotion recognition system works considering entire acquired signal from different channels, which is improper as emotional states do not remain steady for entire durati...

This work proposes deep learning (DL) based epileptic seizure detection by generating 2D recurrence plot (RP) images of EEG signals for specific brain rhythms. The DL bypasses hand-crafted feature engineering, but extracts feature automatically from input images has displayed significant performance in various domain classification tasks. However,...

Numerous studies show that various measures of eye blinking event including blinking rate, duration, blinking rate variability etc. has a strong correlation with mental processes. However, the blinking duration variability (BDV) in connection with different emotional states has not been explored in details. Therefore, this present study investigate...

This work presents a large-scale three-fold annotated, low-cost microscopy image dataset of potato tubers for plant cell analysis in deep learning (DL) framework which has huge potential in the advancement of plant cell biology research. Indeed, low-cost microscopes coupled with new generation smartphones could open new aspects in DL-based microsco...

A method named brain rhythm sequencing has been proposed to interpret Electroencephalography (EEG) signal for emotion recognition. For this purpose, first, the reassigned smoothed pseudo Wigner-Ville distribution (RSPWVD) has been adopted. Then, all generated sequences have been evaluated for classifying the emotional states based on arousal and va...

We present a new large-scale three-fold annotated microscopy image dataset, aiming to advance the plant cell biology research by exploring different cell microstructures including cell size and shape, cell wall thickness, intercellular space, etc. in deep learning (DL) framework. This dataset includes 9,811 unstained and 6,127 stained (safranin-o,...

This work presents a comprehensive review on stimuli presentation, which is an important stage of any emotion elicitation experiment in affect analysis. Due to lack of standard guidelines, the researchers employ their self-devised methods which are not always sufficiently informative — making this area very inconsistent and ambiguous. In addition,...

This work focuses on the quality assessment of agricultural product based on microscopic image, generated by Foldscope. Microscopic image-based food quality assessment always be an efficient method, but its system complexity, costly, bulk size and requirement of special expertise confines it usability. To encounter such issues, Foldscope which is s...

A technique based on five brain rhythms (δ, θ, α, β, and γ) presented in the sequence for analyzing Electroencephalography (EEG) signals has been proposed. First, the production of the sequence has been accomplished by selecting the prominent brain rhythm having the maximum instantaneous power at specific timestamp consecutively throughout the EEG....

Emotion can be regarded as a special brain status and it can be captured by Electroencephalography (EEG) via deploying number of channels all over the scalp. To find out the characteristics of emotional responses, the scalp level connectivity is one of the interests that considered. Nevertheless, to distinguish the significant EEG channels, which c...

This paper proposes a simple approach to measure the elbow joint angle (EJA) using galvanic coupling system (GCS), directly; whereas, the traditional methods involved in either complex machine-learning task or arm movement models in which the consideration of model parameters are not accurate very often. First, a correlation between the EJA and GCS...

This work presents a precise way to detect the third (S3) heart sound, which is recognized as an important indication of heart failure, based on nonlinear single decomposition and time-frequency localization. The detection of the S3 is obscured due to its significantly low energy and frequency. Even more, the detected S3 may be misunderstood as an...

This paper presents an automatic speech-speaker recognition (ASSR) system implemented in a chip which includes a built-in extraction, recognition, and training (ERT) core. For VLSI design (here, ASSR system), the hardware cost and time complexity are always the important issues which are improved in this proposed design in two levels: 1) algorithmi...

In this paper, a no-reference perceptual quality assessment for stereoscopic image is proposed. Inspired by the binocular rivalry mechanism, the observation annoyance perception is explained as a bargain process. Game theory is exploited to model the rivalry of the left eye and right eye. The relation between annoyance perception with binocular dis...

From the past few years, the size of the data grows exponentially with respect to volume, velocity, and dimensionality due to wide spread use of embedded and distributed surveillance cameras for security reasons. In this paper, we have proposed an integrated approach for biometric-based image retrieval and processing which addresses the two issues....

This paper presents an approach to measure the duration and the energy of instantaneous frequencies (EIFs) of the aortic (A2) and pulmonic (P2) valve closure sounds for the second heart sound (S2) based on analytic signal representation. In past studies, concepts were usually surrounded around the measurement of splits (i.e., delays between the A2...

This article presents a novel signal decomposition method, Hilbert vibration decomposition (HVD), for analyzing one of the major heart sound components second heart sound (S2) signal for affective signal modeling. In this proposed method, three kinds of simulated S2 signals are generated and the typical one is chosen for decomposition. For HVD meth...

This work presents a novel feature generation method for automatic heart sound monitoring system based on the nonlinear signal decomposition and the instantaneous characteristics of the decomposed components. In this work, first, the heart sounds (normal and abnormal) are decomposed by complementary ensemble empirical mode decomposition (CEEMD). Ne...

This paper proposes a novel method to improve happiness status by detection negative emotional status based on frowning lines on face and a new term called facial expression factor (FEF). The FEF correlates the frowning and with emotional status. The frowning lines are detected using SOBEL filter and FEF factors are calculated from selected frownin...

This study proposes a quantitative measurement of split of the second heart sound (S2) based on nonstationary signal decomposition to deal with overlaps and energy modeling of the subcomponents of S2. The second heart sound includes aortic (A2) and pulmonic (P2) closure sounds. However, the split detection is obscured due to A2-P2 overlap and low e...

Heart sounds are generated by the several mechanical movements, such as the heart valve leaflets, the blood flow in vessels etc., that can provide the useful information to understand the cardiovascular system in details. Auscultation (PCG) is a non-invasive, low cost and easily repeatable technique to observe the entire system behavior at a glance...