Influence of attention alternation on movement-related cortical potentials in healthy individuals and stroke patients

Effects of divided attention on movement-related cortical potential in community-dwelling elderly adults: A preliminary study

Dual-tasking is defined as performing two or more tasks concurrently. This study aimed to investigate the effect of divided attention on movement-related cortical potential (MRCP) during dual-task performance in 11 community-dwelling elderly individuals while the load of the secondary task was altered. MRCP was recorded during a single task (ST), simple dual task (S-DT), and complex dual task (C-DT) as no-, low-, and high-load divided attention tasks, respectively. The ST involved self-paced tapping with an extended right index finger. In the S-DT and C-DT, the subjects simultaneously performed the ST and a visual number counting task with different levels of load. The coefficient of variation of movement frequency was significantly more variable in the C-DT than in the ST. The MRCP amplitude from electroencephalography electrode C3, contralateral to the moving hand, was significantly higher in the C-DT than in the ST. Higher attention diversion led to a significant reduction in MRCP amplitude in the participants. These results suggest that attention division in dual-task situations plays an important role in movement preparation and execution. We propose that MRCP can serve as a marker for screening the ability of older individuals to perform dual-tasks.

The Effect of Caffeine on Movement-Related Cortical Potential Morphology and Detection

Movement-related cortical potential (MRCP) is observed in EEG recordings prior to a voluntary movement. It has been used for e.g., quantifying motor learning and for brain-computer interfacing (BCIs). The MRCP amplitude is affected by various factors, but the effect of caffeine is underexplored. The aim of this study was to investigate if a cup of coffee with 85 mg caffeine modulated the MRCP amplitude and the classification of MRCPs versus idle activity, which estimates BCI performance. Twenty-six healthy participants performed 2 × 100 ankle dorsiflexion separated by a 10-min break before a cup of coffee was consumed, followed by another 100 movements. EEG was recorded during the movements and divided into epochs, which were averaged to extract three average MRCPs that were compared. Also, idle activity epochs were extracted. Features were extracted from the epochs and classified using random forest analysis. The MRCP amplitude did not change after consuming caffeine. There was a slight increase of two percentage points in the classification accuracy after consuming caffeine. In conclusion, a cup of coffee with 85 mg caffeine does not affect the MRCP amplitude, and improves MRCP-based BCI performance slightly. The findings suggest that drinking coffee is only a minor confounder in MRCP-related studies.

Preparatory movement state enhances premovement EEG representations for brain-computer interfaces

Objective. Motor-related brain-computer interface (BCI) have a broad range of applications, with the detection of premovement intentions being a prominent use case. However, the electroencephalography (EEG) features during the premovement phase are not distinctly evident and are susceptible to attentional influences. These limitations impede the enhancement of performance in motor-based BCI. The objective of this study is to establish a premovement BCI encoding paradigm that integrates the preparatory movement state and validates its feasibility in improving the detection of movement intentions.Methods. Two button tasks were designed to induce subjects into a preparation state for two movement intentions (left and right) based on visual guidance, in contrast to spontaneous premovement. The low frequency movement-related cortical potentials (MRCPs) and high frequency event-related desynchronization (ERD) EEG data of 14 subjects were recorded. Extracted features were fused and classified using task related common spatial patterns (CSP) and CSP algorithms. Differences between prepared premovement and spontaneous premovement were compared in terms of time domain, frequency domain, and classification accuracy.Results. In the time domain, MRCPs features reveal that prepared premovement induce lower amplitude and earlier latency on both contralateral and ipsilateral motor cortex compared to spontaneous premovement, with susceptibility to the dominant hand's influence. Frequency domain ERD features indicate that prepared premovement induce lower ERD values bilaterally, and the ERD recovery speed after button press is the fastest. By using the fusion approach, the classification accuracy increased from 78.92% for spontaneous premovement to 83.59% for prepared premovement (p< 0.05). Along with the 4.67% improvement in classification accuracy, the standard deviation decreased by 0.95.Significance. The research findings confirm that incorporating a preparatory state into premovement enhances neural representations related to movement. This encoding enhancement paradigm effectively improves the performance of motor-based BCI. Additionally, this concept has the potential to broaden the range of decodable movement intentions and related information in motor-related BCI.

Hierarchical Decoding Model of Upper Limb Movement Intention From EEG Signals Based on Attention State Estimation

Decoding the motion intention of the human upper limb from electroencephalography (EEG) signals has important practical values. However, existing decoding models are built under the attended state while subjects perform motion tasks. In practice, people are often distracted by other tasks or environmental factors, which may impair decoding performance. To address this problem, in this paper, we propose a hierarchical decoding model of human upper limb motion intention from EEG signals based on attention state estimation. The proposed decoding model includes two components. First, the attention state detection (ASD) component estimates the attention state during the upper limb movement. Next, the motion intention recognition (MIR) component decodes the motion intention by using the decoding models built under the attended and distracted states. The experimental results show that the proposed hierarchical decoding model performs well under the attended and distracted states. This work can advance the application of human movement intention decoding and provides new insights into the study of brain-machine interfaces.

Effects of a dual task and different levels of divided attention on motor-related cortical potential

[Purpose] The aim of this study was to investigate the effect of divided attention on motor-related cortical potential (MRCP) during dual task performance while the difficulty of the secondary task was altered. [Participants and Methods] Twenty-two right-handed healthy volunteers participated in the study. MRCPs were recorded during two tasks, a single task (ST) and a simple (S-DT) or complex dual task (C-DT). The ST involved a self-paced tapping task in which the participants extended their right index finger. In the dual task, the participants performed the ST and a visual number counting task simultaneously. [Results] The amplitude and integral value of MRCP from electroencephalography electrode C3 was significantly higher in the S-DT than in the ST, whereas they were similar between the C-DT and the ST. Medium-load divided attention (i.e., S-DT) led to significantly more changes in the MRCP magnitude than did low-load divided attention (i.e., ST). However, the MRCP of high-load divided attention (i.e., C-DT) was similar to that of low-load divided attention. [Conclusion] These results suggest that MRCP reflects the function of or network between the supplementary motor area and the dorsolateral prefrontal cortex, and may serve as a marker for screening the capacity of individuals to perform dual tasks.

Adaptive Brain-Computer Interface with Attention Alterations in Patients with Amyotrophic Lateral Sclerosis

The users' mental state such as attention variations can have an effect on the brain-computer interface (BCI) performance. In this project, we implemented an adaptive online BCI system with alterations in the users' attention. Twelve electroencephalography (EEG) signals were obtained from six patients with Amyotrophic Lateral Sclerosis (ALS). Participants were asked to execute 40 trials of ankle dorsiflexion concurrently with an auditory oddball task. EEG channels, classifiers and features with superior offline performance in the training phase of the classification of attention level were selected to use in the online mode for prediction the attention status. A feedback was provided to the users to reduce the amount of attention diversion created by the oddball task. The findings revealed that the users' attention can control an online BCI system and real-time neurofeedback can be applied to focus the attention of the user back onto the main task.

Real-time neurofeedback is effective in reducing diversion of attention from a motor task in healthy individuals and patients with amyotrophic lateral sclerosis

OBJECTIVE

The performance of brain-computer interface (BCI) systems is influenced by the user's mental state, such as attention diversion. In this study, we propose a novel online BCI system able to adapt with variations in the users' attention during real-time movement execution.

APPROACH

Electroencephalography signals were recorded from healthy participants and patients with Amyotrophic Lateral Sclerosis while attention to the target task (a dorsiflexion movement) was drifted using an auditory oddball task. For each participant, the selected channels, classifiers and features from a training data set were used in the online phase to predict the attention status.

MAIN RESULTS

For both healthy controls and patients, feedback to the user on attentional status reduced the amount of attention diversion.

SIGNIFICANCE

The findings presented here demonstrate successful monitoring of the users' attention in a fully online BCI system, and further, that real-time neurofeedback on the users' attention state can be implemented to focus the attention of the user back onto the main task.

Continuous 2D control via state-machine triggered by endogenous sensory discrimination and a fast brain switch

OBJECTIVE

Brain computer interfacing (BCI) is a promising method to control assistive systems for patients with severe disabilities. Recently, we have presented a novel BCI approach that combines an electrotactile menu and a brain switch, which allows the user to trigger many commands robustly and efficiently. However, the commands are timed to periodic tactile cues and this may challenge online control. In the present study, therefore, we implemented and evaluated a novel approach for online closed-loop control using the proposed BCI.

APPROACH

Eleven healthy subjects used the novel method to move a cursor in a 2D space. To assure robust control with properly timed commands, the BCI was integrated within a state machine allowing the subject to start the cursor movement in the selected direction and asynchronously stop the cursor. The brain switch was controlled using motor execution (ME) or imagery (MI) and the menu implemented four (straight movements) or eight commands (straight and diagonal movements).

MAIN RESULTS

The results showed a high completion rate of a target hitting task (~97% and ~92% for ME and MI, respectively), with a small number of collisions, when four-channel control was used. There was no significant difference in outcome measures between MI and ME, and performance was similar for four and eight commands.

SIGNIFICANCE

These results demonstrate that the novel state-based scheme driven by a robust BCI can be successfully utilized for online control. Therefore, it can be an attractive solution for providing the user an online-control interface with many commands, which is difficult to achieve using classic BCI solutions.

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

OBJECTIVE

Brain-computer interface (BCI) systems aim to control external devices by using brain signals. The performance of these systems is influenced by the user's mental state, such as attention. In this study, we classified two attention states to a target task (attended and distracted task level) while attention to the task is altered by one of three types of distractors.

METHODS

A total of 27 participants were allocated into three experimental groups and exposed to one type of distractor. An attended condition that was the same across the three groups comprised only the main task execution (self-paced dorsiflexion) while the distracted condition was concurrent execution of the main task and an oddball task (dual-task condition). Electroencephalography signals were recorded from 28 electrodes to classify the two attention states of attended or distracted task conditions by extracting temporal and spectral features.

RESULTS

The results showed that the ensemble classification accuracy using the combination of temporal and spectral features (spectro-temporal features, 82.3 ± 2.7%) was greater than using temporal (69 ± 2.2%) and spectral (80.3 ± 2.6%) features separately. The classification accuracy was computed using a combination of different channel locations, and it was demonstrated that a combination of parietal and centrally located channels was superior for classification of two attention states during movement preparation (parietal channels: 84.6 ± 1.3%, central and parietal channels: 87.2 ± 1.5%).

CONCLUSION

It is possible to monitor the users' attention to the task for different types of distractors.

SIGNIFICANCE

It has implications for online BCI systems where the requirement is for high accuracy of intention detection.

Symmetry of cortical planning for initiating stepping in sub-acute stroke

OBJECTIVE

This study examined motor planning for stepping when the paretic leg was either stepping or standing (to step with the non-paretic leg), to understand whether difficulty with balance and walking post-stroke could be attributed to poor motor planning.

METHODS

Individuals with stroke performed self-initiated stepping. Amplitude and duration of the movement-related cortical potential (MRCP) was measured from Cz. Electromyography (EMG) of biceps femoris (BF) and rectus femoris (RF) were collected.

RESULTS

There were no differences between legs in stepping speed, MRCP or EMG parameters. The MRCPs when stepping with the paretic leg and the non-paretic leg were correlated. When the paretic leg was stepping, the MRCP amplitude correlated with MRCP duration, indicating a longer planning time was accompanied by higher cognitive effort. Slow steppers had larger MRCP amplitudes stepping with the paretic leg and longer MRCP durations stepping with the non-paretic leg.

CONCLUSIONS

MRCP measures suggest that motor planning for initiating stepping are similar regardless of which limb is stepping. Individuals who stepped slowly had greater MRCP amplitudes and durations for planning.

SIGNIFICANCE

Individuals who step slowly may require more time and effort to plan a movement, which may compromise their safety in the community.