A High-Rate BCI Speller Based on Eye-Closed EEG Signal

Nguyen, Trung-Hau; Yang, Dalin

doi:10.1109/access.2018.2849358

Cited by 32 publications

(13 citation statements)

References 45 publications

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“…However, the complexity of systematical control was almost not mentioned in previous studies. Comparing with state-of-art multi-phase BCI spellers, the performance of our BCI system reaches the same level with those of them [31], [32], [38]. Our system adopts SCP for eliminating mental interference.…”

Section: Discussionmentioning

confidence: 66%

A Novel Real-Time Multi-Phase BCI Speller Based on Sliding Control Paradigm of SSVEP

Cao

Hou

et al. 2019

IEEE Access

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Speller had been proved that it's a kind of well interactive manners for brain computer interface (BCI) system. In this study, we proposed a novel steady-state visual evoked potential (SSVEP) BCI speller developed for numerical input. Based on a previous off-line method of SSVEP recognition, a sliding control protocol was used for our real-time spelling task. For ten subjects, on-line experiments of 10 consecutive number inputs were conducted for two different control conditions. In contrast to traditional static protocol of multi-phase SSVEP signal extraction, the average information transmission rate (ITR) of sliding control protocol reached 23.45 bits/min, higher than that of traditional static protocol (19.85 bits/min). The results showed the validity and high-efficiency of sliding control paradigm for a real-time multi-phase SSVEP speller. INDEX TERMS BCI, speller, multi-phase, sliding control paradigm, SSVEP.

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Section: Discussionmentioning

confidence: 66%

A Novel Real-Time Multi-Phase BCI Speller Based on Sliding Control Paradigm of SSVEP

Cao

Hou

et al. 2019

IEEE Access

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“…According to their results, the best kernel was the cubical one for the three events classification, while the linear kernel was for the spontaneous blinking and voluntary blinking. Another way of using the SVMs is proposed by Nguyen et al 16 A speller was developed with a BCI using the EEG signals with the eyes closed, double blinking. This proposed system was designed to improve the BCI applications that require few commands, as it only uses the closing of the eyes to select and double blinking to undo the action, plus an open eyes event was added.…”

Section: State-of-the-artmentioning

confidence: 99%

Versatile implementation of a hardware–software architecture for development and testing of brain–computer interfaces

Martinez-Ledezma

Barrón-Zambrano

Diaz-Manríquez

et al. 2020

International Journal of Advanced Robotic Systems

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Brain–computer interfaces (BCI) have been focused on improving people’s lifestyles with motor or communication disabilities. However, the utilization of this technology has found news applications, such as increasing human capacities. Nowadays, several researchers are working on probing human capabilities to control several robotic devices simultaneously. The design of BCI is an intricate work that needs a long time to its implementation. For this reason, an architecture to design and implement different types of BCIs is presented in this article. The architecture has a modular design capable of reading various electroencephalography (EEG) sensors and controlling several robotic devices similar to the plug-and-play paradigm. To test the proposed architecture, a BCI was able to manage a hexapod robot and a drone was implemented. Firstly, a mobile robotic platform was designed and implemented. The BCI is based on eye blinking, where a single blinking represents a robot command. The command orders the robot to initiate or stops their locomotion for the hexapod robot. For the drone, a blink represents the takeoff or landing order. The blinking signals are obtained from the prefrontal and frontal regions of the head by EEG sensors. The signals are then filtered using temporal filters, with cutoff frequencies based on delta, theta, alpha, and beta waves. The filtered signals were labeled and used to train a classifier based on the multilayer perceptron (MLP) model. To generate the robot command, the proposal BCI used two models of MLP to ensure the classifier prediction. So, when the two classifiers make the same prediction, within a defined time interval, send the signal to the robot to start or stop its movement. The obtained results show that it is possible to get high precision to control the hexapod robot with a precision of 91.7% and an average of 81.4%.

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“…The former was selected owing to the fact that it is the most popular conventional artificial neural network in classification problems [26], [27]. The latter was chosen owing to its good classification performance among the conventional machine learning algorithms, such as Linear Discriminant Analysis (LDA), and its wide applications [28], [29].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Food Quality Estimation Using Radio Frequency-Powered Sensor Mote

Lam

Nguyen

2020

IEEE Access

Self Cite

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In the past decades, the emerging concern about food safety has led to the increasing demand for monitoring food quality across the world. Aiming towards a novel solution for monitoring food, this study proposes a non-destructive method with self-powering capability for online food monitoring, which can be extendable to IoT applications. Furthermore, the study introduces a novel deep neural network model to predict different states of food quality based on the monitoring results. To monitor the variation in food quality, the paper proposes the detection of total volatile organic compounds (TVOCs) inside the food packages, which have been released during food deterioration. A low-power sensor mote comprised of a capacity humidity sensor and a metal-oxide (MOX) gas sensor was manufactured for this purpose. The self-powering capability of the mote is provided through an energy harvester module, which benefits from the far-field Radio Frequency Energy Harvesting (RFEH) technology. The operating frequency of the module was chosen at the 915-MHz ISM band. The analysis of the harvester performance showed that the harvester could generate 3.3-V dc with an RF input power of as low as-8 dBm, which was sufficient for the mote operation. To verify the proposed solutions, a demonstration to monitor the deterioration of packaged pork and fish was conducted in eight days under ambient and refrigerated storage conditions, using the selfdeveloped RF-powered sensor mote. The raw variations in TVOCs were analyzed to evaluate the reliability of the proposed TVOC-based method. A one-dimensional (1-D) convolutional neural network (CNN) model was trained on the TVOCs dataset to predict different states of food quality. To investigate the applicability of the proposed 1-D CNN to multi-class determination of food quality, two other supervised machine learning algorithms using 2-D inputs, including Multilayer Perceptron (MLP), and Support Vector Machine (SVM), are studied. Their classification accuracies based on the confusion matrix are identified and compared. INDEX TERMS Convolutional neural network (CNN), food quality prediction, radio-frequency energy harvesting, total volatile organic compounds, multilayer perceptron (MLP), support-vector machine (SVM).

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A High-Rate BCI Speller Based on Eye-Closed EEG Signal

Cited by 32 publications

References 45 publications

A Novel Real-Time Multi-Phase BCI Speller Based on Sliding Control Paradigm of SSVEP

A Novel Real-Time Multi-Phase BCI Speller Based on Sliding Control Paradigm of SSVEP

Versatile implementation of a hardware–software architecture for development and testing of brain–computer interfaces

Deep Learning-Based Food Quality Estimation Using Radio Frequency-Powered Sensor Mote

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