This paper aims to develop a speller system based on a bipolar single-channel electroencephalogram with sufficient accuracy. The proposed system consists of a custom-designed headset, a new virtual keyboard with 58 characters, special symbols, and digits, and a five-target steady-state visual-evoked potential (SSVEP)-based brain-computer interface (BCI) utilizing one-dimensional convolutional neural network (1-D CNN) for SSVEP frequency detection. The deep learning model is implemented and trained under the training mode before being applied in the operation mode of the system. To validate the proposed model, we acquire the training dataset with numerous testing conditions, including different frequency resolutions of the feature and different time-window lengths of analysis. Two types of features based on the frequency domain are investigated to compare their performances in terms of classification accuracy of the model. The experimental results from eight subjects shows that on average, the proposed model can classify five-class SSVEP data with a high accuracy of 99.2%. The proposed BCI is then employed in an online experiment of spelling the word ''SPELLER'' using 2-s time window. Consequently, the system achieves an average accuracy of 97.4% and an information transfer rate of 49 ± 7.7 bpm, showing the practicality and feasibility of implementing a reliable single-channel SSVEP-based speller utilizing 1-D CNN.
INDEX TERMSBrain-computer interface (BCI), electroencephalogram (EEG), bipolar single channel, speller, one-dimensional convolutional neural network (1-D CNN), steady-state visual evoked potential (SSVEP).
This study proposes a cost-effective prestress monitoring method for post-tensioned reinforced concrete (RC) beams using a smart strand. Firstly, the concept of a piezoelectric-based smart strand and its implementation for prestress force monitoring are developed. The smart strand is prepared by embedding inexpensive and high-sensitivity electromechanical impedance (EMI) sensors in a steel strand. Next, the feasibility of the proposed method is experimentally verified for prestress force monitoring of a simple supported post-tensioned RC beam. A smart strand prototype is fabricated and embedded into a 6.4 m RC beam which is then prestressed with different levels. For each prestress level, the EMI responses of the smart tendon are measured and the EMI features are extracted for prestress force monitoring. The results showed that the EMI signals of the smart strand showed strong resonant peaks that varied sensitively to the prestress level of the beam. The prestress change in the prestressed RC beam was successfully estimated by using linear regression models of the EMI features.
In this work, we developed a novel system to detect the braking intention of drivers in emergency situations using electroencephalogram (EEG) signals. The system acquired eight-channel EEG and motion-sensing data from a custom-designed EEG headset during simulated driving. A novel method for accurately labeling the training data during an extremely short period after the onset of an emergency stimulus was introduced. Two types of features, including EEG band power-based and autoregressive (AR)-based, were investigated. It turned out that the AR-based feature in combination with artificial neural network classifier provided better detection accuracy of the system. Experimental results for ten subjects indicated that the proposed system could detect the emergency braking intention approximately 600 ms before the onset of the executed braking event, with high accuracy of 91%. Thus, the proposed system demonstrated the feasibility of developing a brain-controlled vehicle for real-world applications.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.