Comparison of Features for Movement Prediction from Single-Trial Movement-Related Cortical Potentials in Healthy Subjects and Stroke Patients

Abstract

Detection of movement intention from the movement-related cortical potential (MRCP) derived from the electroencephalogram (EEG) signals has shown to be important in combination with assistive devices for effective neurofeedback in rehabilitation. In this study, we compare time and frequency domain features to detect movement intention from EEG signals prior to movement execution. Data were recoded from 24 able-bodied subjects, 12 performing real movements, and 12 performing imaginary movements. Furthermore, six stroke patients with lower limb paresis were included. Temporal and spectral features were investigated in combination with linear discriminant analysis and compared with template matching. The results showed that spectral features were best suited for differentiating between movement intention and noise across different tasks. The ensemble average across tasks when using spectral features was (error = 3.4 ± 0.8%, sensitivity = 97.2 ± 0.9%, and specificity = 97 ± 1%) significantly better (P < 0.01) than temporal features (error = 15 ± 1.4%, sensitivity: 85 ± 1.3%, and specificity: 84 ± 2%). The proposed approach also (error = 3.4 ± 0.8%) outperformed template matching (error = 26.9 ± 2.3%) significantly (P > 0.001). Results imply that frequency information is important for detecting movement intention, which is promising for the application of this approach to provide patient-driven real-time neurofeedback.

Classification of EEG signals to identify variations in attention during motor task execution

“…Each group of features was considered in a ten-fold test procedure to design a linear discriminant analysis (LDA) 1 classifier [see details in (Kamavuako et al, 2015)]. In this method nine folds were used for the validation step to 2 obtain the best LDA classifier and one remaining fold was applied to test the classifier.…”

A Hybrid FPGA-Based System for EEG- and EMG-Based Online Movement Prediction

“…Although it has been shown that patients that suffer from, e.g., stroke, are feasible to perform movement-imagination despite chronic or severe motor impairments [ 68 , 69 , 164 ], different kinds of brain damages can affect the EEG [ 165 , 166 , 167 , 168 , 169 , 170 ] and EMG [ 171 , 172 ], and hence, possibly the performance of the proposed system. However, MRCP-based BCIs can be successfully used by patients suffering from stroke [ 173 , 174 , 175 ]. For other neurological diseases, such as amyotrophic lateral sclerosis, it has been shown that certain MRCP characteristics do not differ from healthy patients [ 176 ].…”

Detection of movement intention using EEG in a human-robot interaction environment

“…Different from most classifiers (Bai et al, 2007;Bhagat, 2014;Jiang et al, 2015;Kamavuako et al, 2015;Lew et al, 2012;2014;Nikulin et al, 2008), the detector does not need training data to determine its parameters. The only previous knowledge needed for the SFT detector is the electrophysiological response, i.e., the only characteristic needed is the desynchronization present as a persistent feature related to movement intention, which is used to detect whether the volunteer intended a movement or not.…”

“…Volume 31, Number 4, p. 285-294, 2015 Usually, the detection of movement intention is performed using classifiers that are parameterized, validated and tested in order to obtain the classification accuracy (Bai et al, 2007). In BMI, the most commonly used classifiers include linear discriminant analysis (Kamavuako et al, 2015;Lew et al, 2012;2014), matched filters (Jiang et al, 2015), support vector machines (Bhagat, 2014), common spatial patterns (Nikulin et al, 2008) multivariate linear classifiers (Bai et al, 2011), among others. For successful classification, it is required to have a training record available in order to determine the working parameters of the classifier.…”