Optimizing motor imagery BCI models with hard trials removal and model refinement.

Deep learning models have demonstrated remarkable performance in the classification of motor imagery BCI systems. However, these models exhibit sensitivity to challenging trials, often called hard trials, leading to performance degradation. In this paper, we address this issue by proposing two novel methods for identifying and mitigating the impact of hard trials on model performance.

Computational Study on Effect of KCNQ1 P535T Mutation in a Cardiac Ventricular Tissue.

Heart diseases such as arrhythmia are the main causes of sudden death. Arrhythmias are typically caused by mutations in specific genes, damage in the cardiac tissue, or due to some chemical exposure. Arrhythmias caused due to mutation is called inherited arrhythmia.

Improved Patient-Independent System for Detection of Electrical Onset of Seizures.

Purpose: To design a non-patient-specific system to detect the electrical onset of seizures in patients with temporal lobe epilepsy.

Methods: We used EEG data from 29 seizures of 18 temporal lobe epilepsy patients who underwent multiday video-scalp EEG monitoring as part of their presurgical evaluations. We segmented each data set into preictal and ictal phases, and identified spectral entropy, spectral energy, and signal energy as useful features for discriminating normal and seizure conditions.

Development of a training phantom for compression breast elastography-comparison of various elastography systems and numerical simulations.

The elastic properties of tissue are related to tissue composition and pathological changes. It has been observed that many pathological processes increase the elastic modulus of soft tissue compared to normal. Ultrasound compression elastography is a method of characterization of elastic properties that has been the focus of many research efforts in the last two decades.

A robust adaptive sampling method for faster acquisition of MR images.

A robust adaptive k-space sampling method is proposed for faster acquisition and reconstruction of MR images. In this method, undersampling patterns are generated based on magnitude profile of a fully acquired 2-D k-space data. Images are reconstructed using compressive sampling reconstruction algorithm.

Characterization of biomechanical properties of agar based tissue mimicking phantoms for ultrasound stiffness imaging techniques.

Pathological changes of the body have been observed to change the mechanical properties of soft tissue types which can be imaged by ultrasound elastography. Though initial clinical results using ultrasound elastography in detection of tumors are promising, quantification of signal to noise ratio, resolution and strain image patterns are the best achieved under a controlled study using phantoms with similar biomechanical properties of normal and abnormal tissues. The purpose of this work is to characterize the biomechanical properties of agar based tissue mimicking phantoms by varying the agar concentration from 1.

Study of ultrasound stiffness imaging methods using tissue mimicking phantoms.

A pilot study was carried out to investigate the performance of ultrasound stiffness imaging methods namely Ultrasound Elastography Imaging (UEI) and Acoustic Radiation Force Impulse (ARFI) Imaging. Specifically their potential for characterizing different classes of solid mass lesions was analyzed using agar based tissue mimicking phantoms. Composite tissue mimicking phantom was prepared with embedded inclusions of varying stiffness from 50 kPa to 450 kPa to represent different stages of cancer.