Detection of Movement Intentions through a Single Channel of Electroencephalography

Computational Intelligence and Neuroscience

Jochumsen

Niazi

et al. 2015

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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.

Section: Discussionsupporting

confidence: 82%

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

Computational Intelligence and Neuroscience

Jochumsen

Niazi

et al. 2015

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“…The advantage of using the MRCP as the control signal in an associative BCI is that it can be detected approximately 1-2 seconds prior to movement execution and that the time of the peak negative phase is stable within a patient and session, ensuring precise timing in eliciting the afferent volley. To allow this system to be used in the clinical setting we have worked towards minimizing the number of electrode sites required for reliable detection of the MRCP (Jochumsen et al, 2014). In addition, since it is known that attention can modulate plasticity induction (Stefan et al, 2004), we have investigated the effects of artificially imposed attention shifts on the detection of the MRCP as part of the associative BCI using both spectral and time features (Aliakbaryhosseinabadi et al, 2015;.…”

Section: Introductionmentioning

confidence: 99%

Influence of dual-tasking with different levels of attention diversion on characteristics of the movement-related cortical potential

Aliakbaryhosseinabadi

Jiang

et al. 2017

Brain Research

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Dual tasking is defined as performing two tasks concurrently and has been shown to have a significant effect on attention directed to the performance of the main task. In this study, an attention diversion task with two different levels was administered while participants had to complete a cue-based motor task consisting of foot dorsiflexion. An auditory oddball task with two levels of complexity was implemented to divert the user's attention. Electroencephalographic (EEG) recordings were made from nine single channels. Event-related potentials (ERPs) confirmed that the oddball task of counting a sequence of two tones decreased the auditory P300 amplitude more than the oddball task of counting one target tone among three different tones. Pre-movement features quantified from the movement-related cortical potential (MRCP) were changed significantly between single and dual-task conditions in motor and fronto-central channels. There was a significant delay in movement detection for the case of single tone counting in two motor channels only (237.1-247.4ms). For the task of sequence counting, motor cortex and frontal channels showed a significant delay in MRCP detection (232.1-250.5ms). This study investigated the effect of attention diversion in dual-task conditions by analysing both ERPs and MRCPs in single channels. The higher attention diversion lead to a significant reduction in specific MRCP features of the motor task. These results suggest that attention division in dual-tasking situations plays an important role in movement execution and detection. This has important implications in designing real-time brain-computer interface systems.

“…For a self-paced BCI system designed for neuromodulation that uses the MRCP as the control signal [58,59] we have shown in the past that 9 channels are sufficient for detection of movement intent. Significant plasticity was induced in this associative BCI in healthy participants with very few repetitions and in later studies we demonstrated reasonable a detection accuracy >70% using only a single channel [60]. For clinical applications of this system, it is desirable to minimize the number of channels.…”

Section: B Channel Locationmentioning

confidence: 64%

Classification of Movement Preparation Between Attended and Distracted Self-Paced Motor Tasks

Aliakbaryhosseinabadi

IEEE Trans. Biomed. Eng.

Jiang

et al. 2019

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