Abstract
To provide optional force and speed control parameters for brain–computer interfaces (BCIs), an effective feature extraction method of imagined force and speed of hand clenching based on electroencephalography (EEG) was explored. Twenty subjects were recruited to participate in the experiment. They were instructed to perform three different actual/imagined hand clenching force tasks (4 kg, 10 kg, and 16 kg) and three different hand clenching speed tasks (0.5 Hz, 1 Hz, and 2 Hz). Topographical maps parameters and brain network parameters of EEG were calculated as new features of imagined force and speed of hand clenching, which were classified by three classifiers: linear discrimination analysis, extreme learning machines and support vector machines. Topographical maps parameters were better for recognition of the hand clenching force task, while brain network parameters were better for recognition of the hand clenching speed task. After a combination of five types of features (energy, power spectrum of the autoregressive model, wavelet packet coefficients, topographical maps parameters and brain network parameters), the recognition rate of the hand clenching force task was 97%, and that of the hand clenching speed task was as high as 100%. The brain topographical and the brain network parameters are expected to improve the accuracy of decoding the EEG signal of imagined force and speed of hand clenching. A more efficient brain network may facilitate the recognition of force/speed of hand clenching. Combined features could significantly improve the single-trial recognition rate of imagined forces and speeds of hand clenching. The current study provides a new idea for the imagined force and speed of hand clenching that offers more control intention instructions for BCIs.
Highlights
Brain-computer interfaces (BCIs) are a revolutionary human–computer interaction (Remsik et al 2016; Zhang et al 2016; Ahn and Jun 2015) that are expected to provide potential communication and control applications for specific patients or specific scenes
The motor imagery brain–computer interfaces (BCIs) is an important aspect of the BCIs paradigm (He et al 2015), which is driven by the implicit psychological activity of the subjects, in which an EEG signal is readily detectable in healthy (Yuan and He 2014), as well as disabled, individuals with neuromuscular diseases or injuries, including spinal-cord injury, amyotrophic lateral sclerosis (ALS), and stroke (He et al 2013)
The global field power (GFP) of EEG signals were calculated according to formula (1), which represented the strength of the electric field over the brain at each instant
Summary
Brain-computer interfaces (BCIs) are a revolutionary human–computer interaction (Remsik et al 2016; Zhang et al 2016; Ahn and Jun 2015) that are expected to provide potential communication and control applications for specific patients or specific scenes Many efforts have been devoted to using BCIs to interface with physical devices by bypassing the neuromuscular pathways, including virtual helicopters (Doud et al 2011), physical quadcopters (LaFleur 2013), wheelchairs (Tanaka et al 2005; Carlson and Millan 2013a) and telepresence robots (Carlson et al 2013b). These BCIs have the potential to restore lost or impaired functions of people severely disabled by various devastating neuromuscular disorders or
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