An Effective Brain-Computer Interface System Based on the Optimal Timeframe Selection of Brain Signals

Background: Brain responds in a short timeframe (with certain delay) after the request for doing a motor imagery task and therefore it is most likely that the individual not focus continuously on the task at entire interval of data acquisition time or even think about other things in a very short time slice. In this paper, an effective brain-computer interface system is presented based on the optimal timeframe selection of brain signals.Methods: To prove the stated claim, various timeframes with different durations and delays selected based on a specific rule from EEG signals recorded during right/left hand motor imagery task and subsequently, feature extraction and classification are done.Results: Implementation results on the two well-known datasets termed Graz 2003 and Graz 2005; shows that the smallest systematically created timeframe of data acquisition interval have had the best results of classification. Using this smallest timeframe, the classification accuracy increased up to 91.43% for Graz 2003 and 88.96, 83.64 and 84.86 percent for O3, S4 and X11 subjects of Graz 2005 database respectively.Conclusion: Removing the additional information in which the individual does not focus on the motor imagery task and utilizing the most distinguishing timeframe of EEG signals that correctly interpret individual intentions improves the BCI system performance

Validation of fNIRS System as a Technique to Monitor Cognitive Workload

CognitiveWorkload (CW) is a key factor in the human learning context. Knowing the optimal amount of CW is essential to maximise cognitive performance, emerging as an important variable in e-learning systems and Brain-Computer Interfaces (BCI) applications. Functional Near-Infrared Spectroscopy (fNIRS) has emerged as a promising avenue of brain discovery because of its easy setup and robust results. It is, in fact, along with Electroencephalography (EEG), an encouraging technique in the context of BCI. Brain- Computer Interfaces, by tracking the user’s cognitive state, are suitable for educational systems. Thus, this work sought to validate the fNIRS technique for monitoring different CW stages. For this purpose, we acquired the fNIRS and EEG signals when performing cognitive tasks, which included a progressive increase of difficulty and simulation of the learning process. We also used the breathing sensor and the participants’ facial expressions to assess their cognitive status. We found that both visual inspections of fNIRS signals and power spectral analysis of EEG bands are not sufficient for discriminating cognitive states, nor quantify CW. However, by applying machine learning (ML) algorithms, we were able to distinguish these states with mean accuracies of 79.8%, reaching a value of 100% in one specific case. Our findings provide evidence that fNIRS technique has the potential to monitor different levels of CW. Furthermore, our results suggest that this technique allied with the EEG and combined via ML algorithms is a promising tool to be used in the e-learning and BCI fields for its skill to discriminate and characterize cognitive states.O esforço cognitivo (CW) é um factor relevante no contexto da aprendizagem humana. Conhecer a quantidade óptima de CW é essencial para maximizar o desempenho cognitivo, surgindo como uma variável importante em sistemas de e-learning e aplicações de Interfaces Cérebro-Computador (BCI). A Espectroscopia Funcional de Infravermelho Próximo (fNIRS) emergiu como uma via de descoberta do cérebro devido à sua fácil configuração e resultados robustos. É, de facto, juntamente com a Electroencefalografia (EEG), uma técnica encorajadora no contexto de BCI. As interfaces cérebro-computador, ao monitorizar o estado cognitivo do utilizador, são adequadas para sistemas educativos. Assim, este trabalho procurou validar o sistema de fNIRS como uma técnica de monitorização de CW. Para este efeito, adquirimos os sinais fNIRS e EEG aquando da execução de tarefas cognitivas, que incluiram um aumento progressivo de dificuldade e simulação do processo de aprendizagem. Utilizámos, ainda, o sensor de respiração e as expressões faciais dos participantes para avaliar o seu estado cognitivo. Verificámos que tanto a inspeção visual dos sinais de fNIRS como a análise espectral dos sinais de EEG não são suficientes para discriminar estados cognitivos, nem para quantificar o CW. No entanto, aplicando algoritmos de machine learning (ML), fomos capazes de distinguir estes estados com exatidões médias de 79.8%, chegando a atingir o valor de 100% num caso específico. Os nossos resultados fornecem provas da prospecção da técnica fNIRS para supervisionar diferentes níveis de CW. Além disso, os nossos resultados sugerem que esta técnica aliada à de EEG e combinada via algoritmos ML é uma ferramenta promissora a ser utilizada nos campos do e-learning e de BCI, pela sua capacidade de discriminar e caracterizar estados cognitivos