Combining frequency and time-domain EEG features for classification of self-paced reach-and-grasp actions

Decoding Different Reach-and-Grasp Movements Using Noninvasive Electroencephalogram

Grasping is one of the most indispensable functions of humans. Decoding reach-and-grasp actions from electroencephalograms (EEGs) is of great significance for the realization of intuitive and natural neuroprosthesis control, and the recovery or reconstruction of hand functions of patients with motor disorders. In this paper, we investigated decoding five different reach-and-grasp movements closely related to daily life using movement-related cortical potentials (MRCPs). In the experiment, nine healthy subjects were asked to naturally execute five different reach-and-grasp movements on the designed experimental platform, namely palmar, pinch, push, twist, and plug grasp. A total of 480 trials per subject (80 trials per condition) were recorded. The MRCPs amplitude from low-frequency (0.3-3 Hz) EEG signals were used as decoding features for further offline analysis. Average binary classification accuracy for grasping vs. the no-movement condition peaked at 75.06 ± 6.8%. Peak average accuracy for grasping vs. grasping conditions of 64.95 ± 7.4% could be reached. Grand average peak accuracy of multiclassification for five grasping conditions reached 36.7 ± 6.8% at 1.45 s after the movement onset. The analysis of MRCPs indicated that all the grasping conditions are more pronounced than the no-movement condition, and there are also significant differences between the grasping conditions. These findings clearly proved the feasibility of decoding multiple reach-and-grasp actions from noninvasive EEG signals. This work is significant for the natural and intuitive BCI application, particularly for neuroprosthesis control or developing an active human-machine interaction system, such as rehabilitation robot.

Decoding Hand Movement Types and Kinematic Information From Electroencephalogram

Brain-computer interfaces (BCIs) have achieved successful control of assistive devices, e.g. neuroprosthesis or robotic arm. Previous research based on hand movements Electroencephalogram (EEG) has shown limited success in precise and natural control. In this study, we explored the possibilities of decoding movement types and kinematic information for three reach-and-execute actions using movement-related cortical potentials (MRCPs). EEG signals were acquired from 12 healthy subjects during the execution of pinch, palmar and precision disk rotation actions that involved two levels of speeds and forces. In the case of discrimination between hand movement types under each of four different kinematics conditions, we obtained the average peak accuracies of 83.44% and 73.83% for the binary and 3-class classification, respectively. In the case of discrimination between different movement kinematics for each of three actions, the average peak accuracies of 82.9% and 58.2% could be achieved for the two and 4-class scenario. In both cases, peak decoding performance was significantly higher than the subject-specific chance level. We found that hand movement types all could be classified when these actions were executed at four different kinematic parameters. Meanwhile, for each of three hand movements, we decoded movement parameters successfully. Furthermore, the feasibility of decoding hand movements during hand retraction process was also demonstrated. These findings are of great importance for controlling neuroprosthesis or other rehabilitation devices in a fine and natural way, which would drastically increase the acceptance of motor impaired users.

FINE-GRAINED AND MULTI-SCALE MOTIF FEATURES FOR CROSS-SUBJECT MENTAL WORKLOAD ASSESSMENT USING BI-LSTM

Mental workload (MW) assessment is crucial for understanding human mental state. Cross-subject MW analysis based on electroencephalogram (EEG) signals is an important way. In this paper, a fine-grained and multi-scale motif (FGMSM) features extraction method is proposed, and the proposed features together with original EEG data are used as the input of bidirectional long short-term memory (Bi-LSTM) to evaluate the cross-subject mental workload. First, the EEG signal of each channel is decomposed based on improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) algorithm. Second, for the motif structure consisting of three nodes, multi-scale detection is carried out in each intrinsic mode function, and the proportion of each motif structure is extracted as the newly extracted features. Then, the statistical differences of the extracted features between different MW levels are analyzed by using the [Formula: see text]-test, and the features with statistical differences are selected for the cross-subject MW assessment. Finally, based on the public dataset with 26 subjects, Bi-LSTM and a variety of machine learning algorithms are used to classify the levels of cross-subject MW. The results show that the Bi-LSTM classification method with the original EEG data and the proposed features show the most positive results. Therefore, the FGMSM features proposed in this paper with Bi-LSTM provide a new technique for the assessment of cross-subject MW based on EEG signals.

Decoding hand movements from human EEG to control a robotic arm in a simulation environment

OBJECTIVE

Daily life tasks can become a significant challenge for motor impaired persons. Depending on the severity of their impairment, they require more complex solutions to retain an independent life. Brain-computer interfaces (BCIs) are targeted to provide an intuitive form of control for advanced assistive devices such as robotic arms or neuroprostheses. In our current study we aim to decode three different executed hand movements in an online BCI scenario from electroencephalographic (EEG) data.

APPROACH

Immersed in a desktop-based simulation environment, 15 non-disabled participants interacted with virtual objects from daily life by an avatar's robotic arm. In a short calibration phase, participants performed executed palmar and lateral grasps and wrist supinations. Using this data, we trained a classification model on features extracted from the low frequency time domain. In the subsequent evaluation phase, participants controlled the avatar's robotic arm and interacted with the virtual objects in case of a correct classification.

MAIN RESULTS

On average, participants scored online 48% of all movement trials correctly (3-condition scenario, adjusted chance level 40%, alpha = 0.05). The underlying movement-related cortical potentials (MRCPs) of the acquired calibration data show significant differences between conditions over contralateral central sensorimotor areas, which are retained in the data acquired from the online BCI use.

SIGNIFICANCE

We could show the successful online decoding of two grasps and one wrist supination movement using low frequency time domain features of the human EEG. These findings can potentially contribute to the development of a more natural and intuitive BCI-based control modality for upper limb motor neuroprostheses or robotic arms for people with motor impairments.

Unimanual and Bimanual Reach-and-Grasp Actions Can Be Decoded From Human EEG

While most tasks of daily life can be handled through a small number of different grasps, many tasks require the action of both hands. In these bimanual tasks, the second hand has either a supporting role (e.g. for fixating a jar) or a more active role (e.g. grasping a pot on both handles). In this study we attempt to discriminate the neural correlates of unimanual (performed with left and right hand) from bimanual reach-and-grasp actions using the low-frequency time-domain electroencephalogram (EEG). In a self-initiated movement task, 15 healthy participants were asked to perform unimanual (palmar and lateral grasps with left and right hand) and bimanual (double lateral, mixed palmar/lateral) reach-and-grasps on objects of daily life. Using EEG time-domain features in the frequency range of 0.3-3 Hz, we achieved multiclass-classification accuracies of 38.6 ± 6.6% (7 classes, 17.1% chance level) for a combination of 6 movements and 1 rest condition. The grand average confusion matrix shows highest true positive rates (TPR) for the rest (63%) condition while TPR for the movement classes varied between 33 to 41%. The underlying movement-related cortical potentials (MRCPs) show significant differences between unimanual (e.g left hand vs. right hand grasps) as well unimanual vs. bimanual conditions which both can be attributed to lateralization effects. We believe that these findings can be exploited and further used for attempts in providing persons with spinal cord injury a form of natural control for bimanual neuroprostheses.