A Critical Survey of EEG Based BCI Systems for Applications in Industrial Internet of Things

Industrial Internet of Things (IIoT) and its applications have seen a paradigm shift since the advent of artificial intelligence and machine learning. However, these methods are mostly data-centric and lack flexibility. Brain-Computer Interface (BCI) is envisioned as an efficient solution that may overcome the shortcomings of data-centric techniques by introducing human intuition in the process loop. In this work, we present an exhaustive study on the feasibility of adopting BCI techniques for industrial applications, particularly Electroencephalography (EEG). We present a comprehensive literature survey on the basics of EEG (including signal processing techniques) and its involved paradigms, together with its application scope. We identify the potential use cases of EEG-based BCI systems in industries in the current traction while accounting for their pros and cons. Additionally, we also highlight the existing challenges and propose possible off-the-shelf solutions for overcoming them. We present a two-pronged study (hardware and software specifications) on its deployment by performing lab-scale experiments with a single-channel EEG headset. We compare the metrics of our experiments to 64, 32, and 25 channel data from other studies (collected from the Internet). On average, we observe CPU usage of up to 35%, memory usage of 1.75 Gb, and data transfer rates of 25 Mbps. In contrast to the conventional setups, we demonstrate how a minimalistic setup using a single-channel EEG headset may facilitate complex applications like job inspection in manufacturing processes, with an overall accuracy of 70%.