Biomedical Signal Acquisition Using Sensors under the Paradigm of Parallel Computing

A BCI framework for smart home automation using EEG signal

Since the first recording in 1924, modern developments in technology have enabled human electroencephalogram (EEG) acquisition as a non-invasive process, enabling a multitude of opportunities to learn about human brain dynamics. With the capability to tap into localized brain dynamics, it is possible to correlate event-related potentials such as meditation, concentration, and motor control with localized brain activities, opening up a broad spectrum for exploration and implementation in prosthetic, control, and brain computer interfaces. In this work, an attempt has been made to explore human emotions and other intelligent states that can be interpreted to automate and control electrical appliances for differently abled people. A smart home automation model has been designed and implemented using a Think Gear Application-specific integrated circuit (ASIC) Module (TGAM) EEG sensor module integrated with a Bluetooth module, which serves as the core for control applications. A combination of external triggers and brain states are interpreted and forwarded to gain control of the connected appliances via a local server over the internet. Equipped with internet connectivity and Internet of Things (IoT), the system also facilitates long-distance communication and control, which can be easily translated to industrial control and automation applications. Based on a single-channel analog EEG signal acquisition module, this study details the development of a Brain Computer Interface (BCI) control and monitoring system for smart home automation with blink and attention features. The designed BCI system has a large bandwidth of 400 Hz, an easy setup, Morphological Component Analysis (MCA) based blink detection, monitoring, control of devices, and high accuracy at a low cost. Each subject completed three trials separated by one minute. The smart devices underwent testing in two states, namely on and off. The response time and accuracy of the system were recorded for each trial. The average system response time for the devices was determined to be 17.13 sec for switching ON and 20.66 sec for switching OFF, with an accuracy of 98.73% and 95.75% for ON and OFF states respectively.

Deep Learning versus Spectral Techniques for Frequency Estimation of Single Tones: Reduced Complexity for Software-Defined Radio and IoT Sensor Communications

Despite the increasing role of machine learning in various fields, very few works considered artificial intelligence for frequency estimation (FE). This work presents comprehensive analysis of a deep-learning (DL) approach for frequency estimation of single tones. A DL network with two layers having a few nodes can estimate frequency more accurately than well-known classical techniques can. While filling the gap in the existing literature, the study is comprehensive, analyzing errors under different signal-to-noise ratios (SNRs), numbers of nodes, and numbers of input samples under missing SNR information. DL-based FE is not significantly affected by SNR bias or number of nodes. A DL-based approach can properly work using a minimal number of input nodes N at which classical methods fail. DL could use as few as two layers while having two or three nodes for each, with the complexity of O{N} compared with discrete Fourier transform (DFT)-based FE with O{Nlog2 (N)} complexity. Furthermore, less N is required for DL. Therefore, DL can significantly reduce FE complexity, memory cost, and power consumption, which is attractive for resource-limited systems such as some Internet of Things (IoT) sensor applications. Reduced complexity also opens the door for hardware-efficient implementation using short-word-length (SWL) or time-efficient software-defined radio (SDR) communications.