EEG-based hybrid QWERTY mental speller with high information transfer rate

Katyal, Er. Akshay; Singla, Rajesh

doi:10.1007/s11517-020-02310-w

Cited by 10 publications

(10 citation statements)

References 59 publications

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“…where N is number of stimulus, P is average performance and t indicates time for each selection. [21]. Besides, a small number of stimuli in effective BCI systems is expected to have a low ITR value.…”

Section: Resultsmentioning

confidence: 99%

“…In this study, unlike the traditional p300 paradigm, a fixed order stimulus demonstration method is applied. There are the proposed similar approaches in previous studies [12,21]. The purpose of this is to respond to the focused stimulus with the help of a single stimulus series, rather than determining which stimulus occurs more in the P300 signal with many stimulus trials.…”

Section: Visual Stimuli Matrixmentioning

confidence: 99%

See 1 more Smart Citation

Classification of P300 based brain computer interface systems using long short-term memory (LSTM) neural networks with feature fusion

Selvi¹,

Ferikoğlu²,

Erdoğan³

2021

Turk J Elec Eng & Comp Sci

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Enabling to obtain brain activation signs, electroencephalography is currently used in many applications as a medical diagnostic method. Brain-Computer Interface (BCI) applications are developed to facilitate the lives of individuals who have not lost their brain functions, yet have lost their motor and communication abilities. In this study, a BCI system is proposed to classify using Bi-directional Long Short Term Memory (Bi-LSTM) neural networks.In the designed system, spectral entropy method including instantaneous frequency change of signal is used as feature fusion. In the study, EEG data of 10 participants are collected with Emotiv EPOC+ device using 2x2 visual stimulus matrix prepared on Unity. Each symbol of the 2x2 matrix includes stimulus such as Doctor, Police, Fireman and Family.These stimuluses are demonstrated to participants with a fixed order. As data collection protocol, 200ms stimulus time and 300ms interstimulus interval are used. As the performance success of classification, the average accuracy rates are obtained to be 98.6% for training set and 96.9% for the test set. In addition, in classification of P300 EEG signals, the results obtained via Bi-LSTM are compared with the results obtained using 1 Dimensional Convolutional Neural Networks (1DCNN) and Support Vector Machines (SVM) classification methods. Moreover, in the study, information transfer rate (ITR) is provided as 40.39 at an acceptable level.

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Section: Resultsmentioning

confidence: 99%

Section: Visual Stimuli Matrixmentioning

confidence: 99%

Classification of P300 based brain computer interface systems using long short-term memory (LSTM) neural networks with feature fusion

Selvi¹,

Ferikoğlu²,

Erdoğan³

2021

Turk J Elec Eng & Comp Sci

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show abstract

“…The QWERTY keyboard layout is that the characters and numbers are arranged in the sequence as the standard keyboard. In Katyal and Singla ( 2021 ), Katyal and Singla added the P300 signal into the QWERTY SSVEP speller and achieved a relatively high ITR. In their hybrid speller paradigm, the 36 symbols in the traditional QWERTY SSVEP speller were separated into two sets, one of which included four groups and the other of which included five groups of symbols.…”

Section: Methodsmentioning

confidence: 99%

Advances in P300 brain–computer interface spellers: toward paradigm design and performance evaluation

Pan

Chen

Ban

et al. 2022

Front. Hum. Neurosci.

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A brain-computer interface (BCI) is a non-muscular communication technology that provides an information exchange channel for our brains and external devices. During the decades, BCI has made noticeable progress and has been applied in many fields. One of the most traditional BCI applications is the BCI speller. This article primarily discusses the progress of research into P300 BCI spellers and reviews four types of P300 spellers: single-modal P300 spellers, P300 spellers based on multiple brain patterns, P300 spellers with multisensory stimuli, and P300 spellers with multiple intelligent techniques. For each type of P300 speller, we further review several representative P300 spellers, including their design principles, paradigms, algorithms, experimental performance, and corresponding advantages. We particularly emphasized the paradigm design ideas, including the overall layout, individual symbol shapes and stimulus forms. Furthermore, several important issues and research guidance 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.

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“…Brain-computer interfaces (BCIs) based on electroencephalograms (EEGs) have been extensively researched in recent decades to assist challenged individuals in communicating with the outside world [2]. Various BCI applications, such as mental spellers [3], patient-assistant systems [4], neurorehabilitation [5], and external device control [6] have been developed for these people, showing the potential of EEG-based BCIs as practical assistance tools. The EEG headset converts data into packets and transmits them to a Personal Computer (PC) over a wireless medium (Bluetooth).…”

Section: Introductionmentioning

confidence: 99%

Design and Development of Low-cost Wearable Electroencephalograms (EEG) Headset

Riaz¹,

Ali²,

Anwar³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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Electroencephalogram (EEG) is a method of capturing the electrophysiological signal of the brain. An EEG headset is a wearable device that records electrophysiological data from the brain. This paper presents the design and fabrication of a customized low-cost Electroencephalogram (EEG) headset based on the open-source OpenBCI Ultracortex Mark IV system. The electrode placement locations are modified under a 10-20 standard system. The fabricated headset is then compared to commercially available headsets based on the following parameters: affordability, accessibility, noise, signal quality, and cost. First, the data is recorded from 20 subjects who used the EEG Headset, and signals were recorded. Secondly, the participants marked the accuracy, set up time, participant comfort, and participant perceived ease of set-up on a scale of 1 to 7 (7 being excellent). Thirdly, the self-designed EEG headband is used by 5 participants for slide changing. The raw EEG signal is decomposed into a series of band signals using discrete wavelet transform (DWT). Lastly, these findings have been compared to previously reported studies. We concluded that when used for slide-changing control, our self-designed EEG headband had an accuracy of 82.0 percent. We also concluded from the results that our headset performed well on the cost-effectiveness scale, had a reduced setup time of 2 ± 0.5 min (the shortest among all being compared), and demonstrated greater ease of use.

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EEG-based hybrid QWERTY mental speller with high information transfer rate

Cited by 10 publications

References 59 publications

Classification of P300 based brain computer interface systems using long short-term memory (LSTM) neural networks with feature fusion

Classification of P300 based brain computer interface systems using long short-term memory (LSTM) neural networks with feature fusion

Advances in P300 brain–computer interface spellers: toward paradigm design and performance evaluation

Design and Development of Low-cost Wearable Electroencephalograms (EEG) Headset

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