Teaching

The course provides a theoretical basis of advanced analog and integrated circuits, especially operational amplifier implementations for applications. It also introduces safe handling practices of electronic lab equipment to students. Circuit analysis, conventions; Thevenin's and Norton's equivalents; Characteristics, large & small signal models of MOSFET, BJT, Op-amps; Operational amplifiers - mathematical operations, active filter implementations - single & two-pole, Sallen-Key; Differential amplifier & Instrumentation amplifier implementations. 

This course will introduce neurotechnology, especially neuroimaging, neuromodulation and Brain machine interfaces (BMI). Neuroimaging encompasses use of electrophysiology techniques like EEG and fusion of functional activity with anatomical information. Neuromodulation is the large variety of techniques used to assist, treat injuries to various parts of the nervous system. BCI on the other hand is a channel of communication between a brain and a machine (typically a computer). These techniques have a variety of medical as well as computer applications. Students are also expected to gain insight into circuitry involved in movement in healthy and disorder cohorts such as Parkinson's disease. The neuroscientific concepts that help entrain subjects to learn from abstract cues and rewards will be discussed. Mathematical models and stochastic processes to capture the learning processes will be discussed. Various modalities to record the subject's intent such as EEG, MEG, fNIRS, EMG will be discussed. 

This course is meant to provide practical insight into using neurotechnology, especially EEG and other electrophysiology tools to implement real-time processing algorithms, machine learning techniques to implement a lean brain-computer interface. Students are expected to gain end-to-end knowledge of the field of neuroimaging, neuromodulation, BMI and how signal processing and machine learning concepts can be applied to build better BCI systems. EEG, fNIRS and EMG will be used to implement BCI and discussed. Futhermore, lab component will include design of BMI system with real-time processing of electrophysiological signals along with feedback. Students are expected to implement basic acquisition, real-time processing and establish an interface with openBCI framework and perform experiments of brain control of external devices. 

This course is intended to understand the origin of signals in biosystems and living organisms, their sensing, detection and meaningful processing for practical diagnostic sensing applications. Various engineering aspects of the detection, acquisition, processing, and display of signals, biomedical sensors for measurements of biopotentials such as ECG, displacement sensors, gas analyzers will be addressed in this course. The course includes work involving basic electronics, sensor design and interfaces for building complete biomedical instrumentation. Generalized design of bioinstruments, noise suppression techniques, Bioinstrument characteristics – basics of regulatory processes. Amplifiers & filters. Sensors & signal conditioning circuits for displacement sensing, gas analyzers, pulse oximetry. Principles bioelectric transduction, ECG-origin, physiology, 12-lead system, instrumentation, rhythms, arrhythmias, pathology, diagnostics. Simulators for pacemaker, defibrillator, heart rate detection. 

This course is intended to impart understanding of various biosignals, explain their origin, sensing, signal conditioning and meaningful representation. Temperature, physiological pressures and flows, volume sensing, bioelectricity from muscles and eyes, the related biological systems and their physiology will be discussed. The course includes work involving advanced linear integrated circuit design, interfacing and real-time processing of biosignals and understanding the diagnostic value in biosignals will be discussed. Physiological pressures, sounds and flows: cardio-pulmonary system, non-invasive measurements. Flow and volume sensing in pulmonary function - sensing of the mechanics of breathing, spirometer interface with LabVIEW, computation of volumes and capacities during a pulmonary function test, mechanical ventilation, applications. Bioelectric sensing: Electromyography - Origin, processing, detection of muscle fatigue, nerve conduction velocity test.