This thesis presents a bio-signal recording system with offset cancellation and a low power comparator. The recording of bio-signal requires high-gain amplification before recording, to match the input to the range of the analog to digital converter (ADC); interference could be a problem if it causes the amplifier to reach saturation, leaving the recording inoperable (i.e., blank) until it returns to its normal state. The proposed system can monitor the amplifier output, and reset the amplifier output to a point near the center of its dynamic range before the amplifier output leaves its dynamic range. The proposed system provides discrete compensation voltages to cancel the offset voltage, and thereby avoids the shortcomings of conventional filters. Furthermore, a low power and low offset voltage comparator for low current operation is proposed. It is suitable for the clock controller in a sampled bio-signal acquisition system. The measured current consumption of the comparator is less than 130 nA, and the offset voltage is 2 mV. The proposed recording system and comparator have been implemented in the TSMC (Taiwan Semiconductor Manufacturing Company) 0.35£gm 2P4M CMOS process technology to verify the simulation results as well as the correctness of the proposed architecture.

The recent evolution in multidisciplinary fields of Engineering, neuroscience, microelectronics, bioengineering and neurophysiology have reduced the gap between human and machine intelligence. Many methods and algorithms have been developed for analysis and classification of bio signals, 1 or 2-dimensional, in time or frequency distribution. The integration of signal processing with the electronic devices serves as a major root for the development of various biomedical applications. There are many ongoing research in this area to constantly improvise and build an efficient human- robotic system. Electroencephalography (EEG) technology is an efficient way of recording electrical activity of the brain. The advancement of EEG technology in biomedical application helps in diagnosing various brain disorders as tumors, seizures, Alzheimer’s disease, epilepsy and other malfunctions in human brain. The main objective of our thesis deals with acquiring and pre-processing of real time EEG signals using a single dry electrode placed on the forehead. The raw EEG signals are transmitted in a wireless mode (Bluetooth) to the local acquisition server and stored in the computer. Various machine learning techniques are preferred to classify EEG signals precisely. Different algorithms are built for analysing various signal processing techniques to process the signals. These results can be further used for the development of better Brain-computer interface systems.

Traditionally robots are controlled using devices like joysticks, keyboards, mice and other similar human computer interface (HCI) devices. Although this approach is effective and practical for some cases, it is restrictive only to healthy individuals without disabilities, and it also requires the user to master the device before its usage. It becomes complicated and non-intuitive when multiple robots need to be controlled simultaneously with these traditional devices, as in the case of Human Swarm Interfaces (HSI). This work presents a novel concept of using human bio-signals to control swarms of robots. With this concept there are two major advantages: Firstly, it gives amputees and people with certain disabilities the ability to control robotic swarms, which has previously not been possible. Secondly, it also gives the user a more intuitive interface to control swarms of robots by using gestures, thoughts, and eye movement. We measure different bio-signals from the human body including Electroencephalography (EEG), Electromyography (EMG), Electrooculography (EOG), using off the shelf products. After minimal signal processing, we then decode the intended control action using machine learning techniques like Hidden Markov Models (HMM) and K-Nearest Neighbors (K-NN). We employ formation controllers based on distance and displacement to control the shape and motion of the robotic swarm. Comparison for ground truth for thoughts and gesture classifications are done, and the resulting pipelines are evaluated with both simulations and hardware experiments with swarms of ground robots and aerial vehicles.

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