P300 Speller Bci Project — Biomedical Instrumentation & Clinical Sensing

Abstract — P300 Speller Bci Project

for the P300 speller were identified. We hope that this review can assist researchers in learning the new ideas of these novel P300 spellers and enhance their practical application capability. A brain-computer interface (BCI) is a technology that directly controls external devices by analyzing the electrical signals sent by the nerves of the brain (Wolpaw et al., 2000 ; Wolpaw, 2007 ). It was developed to offer patients with motor neuron disease (MND; Hanagasi et al., 2002 ), including amyotrophic lateral sclerosis (ALS) and locked-in syndrome, a way to communicate with the outside world and regain their social life to a relatively high extent. There are many types of BCI systems related to the detection method of brain activities, including those based on electroencephalography (EEG), near-infrared spectroscopy, magnetoencephalography, and functional magnetic resonance (He and Liu, 2008 ; Millán and Carmena, 2010 ), and the most common are EEG-based BCI systems due to their features of noninvasiveness, portability and high signal responses (Wolpaw et al., 1991 ; De Vos et al., 2014 ). EEG signals included P300 (Sellers et al., 2006 ; Kleih and Kübler, 2013 ; Xu M. et al., 2013 ), steady-state visual evoked potential (SSVEP; Müller-Putz and Pfurtscheller, 2008 ; Wu et al., 2008 ; Jalilpour et al., 2020 ), and motor imagery (MI; Devlaminck et al., 2011 ; Park et al., 2012 ; Kevric and Subasi, 2017 ). P300 is a positive event related potential (ERP) component that can be generated during an oddball paradigm. In an oddball paradigm, users were presented with a sequence of events that could be divided into two categories (target and non-target), one of which was rarely presented. The P300 is detected 300 ms after the occurrence of a small probability event stimulus (Sutton et al., 1965 ).

Sensor Interfacing & Analog Front-End Conditioning

SSVEP is a signal induced by presenting a visual stimulus with a specific flickering frequency. P300 and SSVEPs are commonly used EEG signals that do not require training on subjects, and the information transfer rate (ITR) is relatively high. MI is an EEG signal that the subject can emit without external stimulation (Rao, 2013 ).

Bio-Signal Waveform Analysis & Simulation Findings

At present, BCI is used in many fields, including communication (Kennedy and Bakay, 1998 ; Hochberg et al., 2006 ), medical rehabilitation (Rohani and Puthusserypady, 2015 ), home automation (Corralejo et al., 2014 ; Park et al., 2015 ), and game entertainment (Leeb et al., 2013 ). The BCI speller is one of the most common applications and has been developed for decades. Different types of spellers based on various EEG signals have been studied. The P300-based BCI speller is the most common BCI speller. The earliest BCI speller is a P300-based speller proposed by Farwell and Donchin, which is the basis of many researchers conducted later. Another relatively common type of BCI speller is an SSVEP-based BCI speller. In addition, there are BCI spellers based on MI, and now there are some spellers that combine two EEG signals, such as P300+SSVEP and P300+MI.

Scope & Clinical Applications

• Real-time multi-parameter vital signs telemetry (ECG, Heart Rate, SpO2, Temperature, Respiration)
• Low-noise Analog Front-End (AFE) conditioning with active Driven Right Leg (DRL) 50 Hz cancellation
• Digital signal processing in MATLAB: Pan-Tompkins QRS detection, FIR/IIR notch, and Continuous Wavelet Transforms
• Finite element tissue bio-electromagnetics and orthopedic mechanical simulations in COMSOL & ANSYS
• Complete Arduino firmware .INO sketches, circuit schematics, and university dissertation reports