Abstract
The traditional imagery task for brain–computer interfaces (BCIs) consists of motor imagery (MI) in which subjects are instructed to imagine moving certain parts of their body. This kind of imagery task is difficult for subjects. In this study, we used a less studied yet more easily performed type of mental imagery—visual imagery (VI)—in which subjects are instructed to visualize a picture in their brain to implement a BCI. In this study, 18 subjects were recruited and instructed to observe one of two visual-cued pictures (one was static, while the other was moving) and then imagine the cued picture in each trial. Simultaneously, electroencephalography (EEG) signals were collected. Hilbert–Huang Transform (HHT), autoregressive (AR) models, and a combination of empirical mode decomposition (EMD) and AR were used to extract features, respectively. A support vector machine (SVM) was used to classify the two kinds of VI tasks. The average, highest, and lowest classification accuracies of HHT were 68.14 ± 3.06%, 78.33%, and 53.3%, respectively. The values of the AR model were 56.29 ± 2.73%, 71.67%, and 30%, respectively. The values obtained by the combination of the EMD and the AR model were 78.40 ± 2.07%, 87%, and 48.33%, respectively. The results indicate that multiple VI tasks were separable based on EEG and that the combination of EMD and an AR model used in VI feature extraction was better than an HHT or AR model alone. Our work may provide ideas for the construction of a new online VI-BCI.
Highlights
Brain–computer interfaces (BCIs) represent revolutionary human–computer interactions that aim to bypass peripheral nerves and muscles of the spinal cord and neuromusculature to realize direct communication and control between brains and the outside world. is technology is expected to provide an alternative new communication or control method for patients with severe movement disabilities or for healthy people with ad hoc needs for BCIs.BCIs based on imagery represent an important type of BCI [1]. e traditional imagery task is motor imagery (MI) [2, 3], which requires subjects to imagine moving a certain part of their body from a first-person perspective [4, 5]
A t-test was carried out to determine which channel had a significant difference. e results showed that the p values of positions F8 and FP2 were less than 0.01, which meant that there was a 99% significant difference between the data groups, which was the highest among all of the results
visual imagery (VI) tasks are easier to acquire and control compared to MI tasks
Summary
Brain–computer interfaces (BCIs) represent revolutionary human–computer interactions that aim to bypass peripheral nerves and muscles of the spinal cord and neuromusculature to realize direct communication and control between brains and the outside world. is technology is expected to provide an alternative new communication or control method for patients with severe movement disabilities or for healthy people with ad hoc needs for BCIs.BCIs based on imagery represent an important type of BCI [1]. e traditional imagery task is motor imagery (MI) [2, 3], which requires subjects to imagine moving a certain part of their body from a first-person perspective [4, 5]. Brain–computer interfaces (BCIs) represent revolutionary human–computer interactions that aim to bypass peripheral nerves and muscles of the spinal cord and neuromusculature to realize direct communication and control between brains and the outside world. E traditional imagery task is motor imagery (MI) [2, 3], which requires subjects to imagine moving a certain part of their body from a first-person perspective [4, 5]. MI may not be the best mental task for controlling BCIs [1]. Compared with the properties of MI, visual imagery (VI) is another mental-imagery task that is easier to complete, and it consists of instructing subjects to visualize a picture clearly in their brain from a third-person perspective [4].
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