Objective. Brain–computer interfaces (BCI) are becoming important tools for assistive technology, particularly through the use of motor imagery (MI) for aiding task completion. However, most existing methods of MI classification have been applied in a trial-wise fashion, with window sizes of approximately 2 s or more. Application of this type of classifier could cause a delay when switching between MI events. Approach. In this study, state-of-the-art classification methods for motor imagery are assessed offline with considerations for real-time and self-paced control, and a convolutional long-short term memory (C-LSTM) network based on filter bank common spatial patterns (FBCSP) is proposed. In addition, the effects of several methods of data augmentation on different classifiers are explored. Main results. The results of this study show that the proposed network achieves adequate results in distinguishing between different control classes, but both considered deep learning models are still less reliable than a Riemannian MDM classifier. In addition, controlled skewing of the data and the explored data augmentation methods improved the average overall accuracy of the classifiers by 14.0% and 5.3%, respectively. Significance. This manuscript is among the first to attempt combining convolutional and recurrent neural network layers for the purpose of MI classification, and is also one of the first to provide an in-depth comparison of various data augmentation methods for MI classification. In addition, all of these methods are applied on smaller windows of data and with consideration to ambient data, which provides a more realistic test bed for real-time and self-paced control.
Monitoring the mental workload of operators is of paramount importance in space telerobotic training and other teleoperation tasks. Instead of the estimation of taskspecific workload, this paper aimed at investigating the impact of two significant confounding factors (time-pressure and latency) on space teleoperation and explored the use of eye-tracking technology for factor-induced mental workload estimation and performance evaluation. Ten subjects teleoperated a Canadarm2 robot to complete a complex on-orbit assembly task in our photo-realistic training simulator while wearing a head-mounted eye-tracker. To understand how time-pressure and latency influence eye-tracking features, we first performed the statistical analysis on various features with respect to a single factor and across multiple groups. Next, eye-tracking features extracted from segment data and trial data were used to identify the mental workload induced by confounding factors, which can be used for developing personalized training programs and guaranteeing safe teleoperation. Furthermore, to improve the recognition performance using segment data, we proposed the activity ratio and time ratio to characterize the informative segments. Finally, the relationship between simulator-defined performance measures and eye-tracking features was examined. Results showed that fixation duration, saccade frequency and duration, pupil diameter, and index of pupillary activity are significant features that can be used in both factor-induced mental workload estimation and task performance evaluation.
Traditional methods of training a Brain-Computer Interface (BCI) on motor imagery (MI) data generally involve multiple intensive sessions. The initial sessions produce simple prompts to users, while later sessions additionally provide realtime feedback to users, allowing for human adaptation to take place. However, this protocol only permits the BCI to update between sessions, with little real-time evaluation of how the classifier has improved. To solve this problem, we propose an adaptive BCI training framework which will update the classifier in real time to provide more accurate feedback to the user on 4class motor imagery data. This framework will require only one session to fully train a BCI to a given subject. Three variations of an adaptive Riemannian BCI were implemented and compared on data from both our own recorded datasets and the commonly used BCI Competition IV Dataset 2a. Results indicate that the fastest and least computationally expensive adaptive BCI was able to correctly classify motor imagery data at a rate 5.8% higher than when using a standard protocol with limited data. In addition it was confirmed that the adaptive BCI automatically improved its performance as more data became available.
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