Brain-computer interface, widely known as BCI, is a relatively new field of research that has emerged as promising field research in the last few decades. It is defined as a combination of software as well as hardware that give us the tool to control external devices by using our brain signals as commands. In this chapter, the authors discuss the various tools that can be used to analyze and perform different functions on the brain signals, create BCI models, simulations, etc. In this study, they compare the tools and tabulate how they might be useful for the user's requirements. Additionally, they have implemented the use of tools for real-time applications. The experimental analysis presented in this work utilizes MAMEM EEG steady-state visually evoked potential (SSVEP) dataset I. Five different frequencies (6.66, 7.50, 8.57, 10.00, and 12.00 Hz) were used for the visual stimulation. The authors have analyzed different parameters like power spectrum density, power spectrum, and inter-trial coherence (ITC) through EEGLAB.
TopIntroduction
Brain-Computer Interface (BCI) is a communication mechanism that does not rely on the normal peripheral nervous and muscular production pathways of the brain. BCI 's ultimate aim is to develop a standardized interface to allow a person with serious motor disabilities to have effective control of devices such as computers, speech synthesizers, prostheses (Dornhege et al., 2007 & Blankertz et al., 2010) and home appliance (Shukla et al., 2020). In other words, BCI describes a system of contact and control between the human brain and computers designed to assist disabled people using their electrical brain activity, which is normally monitored using electroencephalogram (EEG) (Vaid et al., 2015). There are various other ways of recording brain activity like Electrocorticography (ECoG), Near-Infrared chemical analysis (NIRS), practical Resonance Imaging (fMRI), etc. EEG is one of the foremost wide used because of high temporal resolution, ease of use, safe, and high affordability. There are two techniques namely invasive and non-invasive can be used to measure the electrical activities of the brain. In invasive technique, the sensors are placed inside the brain to increase the information in the acquired data. The goal is to target the neurons within a specific brain area and record the high-frequency neural signals. In non-invasive technique (Szafir et al., 2010), the signals are captured without any penetration of the scalp thus avoiding surgery. Recorded signals through noninvasive technique have low frequency and comparatively poor spatial resolution. The non-invasive technique has the chances of localization and the information in addition to low information content.
Our brain generates various types of signals, classified into three main classes, 1) Evoked signals–the signals unconsciously generated to external stimuli, For Example: State Evoked Potentials (SSEP) which are of two types, a) steady-state visually evoked potentials (SSVEP) which occur after looking at a visual stimulus flickering with constant frequency signals, have a frequency range of 3.5–75 Hz and can be divided into bands, according to their frequency (Regan et al., 1975). b) auditory steady-state responses (ASSRs), induced by repetitive auditory stimuli (Galambos et al., 1981); c) Somatosensory steady-state evoked potentials (SSSEPs), observed as a response to a repetitive tactile stimulation; and P300, it is detected with a latency of roughly 300ms after the use of stimuli (Gonzalez et al., 2016); 2)Spontaneous signals-Signals that are generated voluntarily without any external stimuli, It is of two main types, a) Motor and sensorimotor rhythms (8-26 Hz) (Cheyne et al., 2013), b)Slow Cortial Potentials(SCP) (frequency < 1Hz) (Birbaumer et al., 1999); 3)Hybrid signals- Where 2 or more types of brain signals are used as an input to a system (Yin et al., 2015). Brain signals can be used in different application. The detail of the brain signals and its application shown in Table.1