A study on decoding models for the reconstruction of hand trajectories from the human magnetoencephalography

Decoding neural signals into control outputs has been a key to the development of brain-computer interfaces (BCIs). While many studies have identified neural correlates of kinematics or applied advanced machine learning algorithms to improve decoding performance, relatively less attention has been paid to optimal design of decoding models. For generating continuous movements from neural activity, design of decoding models should address how to incorporate movement dynamics into models and how to select a model given specific BCI objectives. Considering nonlinear and independent speed characteristics, we propose a hybrid Kalman filter to decode the hand direction and speed independently. We also investigate changes in performance of different decoding models (the linear and Kalman filters) when they predict reaching movements only or predict both reach and rest. Our offline study on human magnetoencephalography (MEG) during point-to-point arm movements shows that the performance of the linear filter or the Kalman filter is affected by including resting states for training and predicting movements. However, the hybrid Kalman filter consistently outperforms others regardless of movement states. The results demonstrate that better design of decoding models is achieved by incorporating movement dynamics into modeling or selecting a model according to decoding objectives.open0

Studies to Overcome Brain–Computer Interface Challenges

A brain–computer interface (BCI) is a promising technology that can analyze brain signals and control a robot or computer according to a user’s intention. This paper introduces our studies to overcome the challenges of using BCIs in daily life. There are several methods to implement BCIs, such as sensorimotor rhythms (SMR), P300, and steady-state visually evoked potential (SSVEP). These methods have different pros and cons according to the BCI type. However, all these methods are limited in choice. Controlling the robot arm according to the intention enables BCI users can do various things. We introduced the study predicting three-dimensional arm movement using a non-invasive method. Moreover, the study was described compensating the prediction using an external camera for high accuracy. For daily use, BCI users should be able to turn on or off the BCI system because of the prediction error. The users should also be able to change the BCI mode to the efficient BCI type. The BCI mode can be transformed based on the user state. Our study was explained estimating a user state based on a brain’s functional connectivity and a convolutional neural network (CNN). Additionally, BCI users should be able to do various tasks, such as carrying an object, walking, or talking simultaneously. A multi-function BCI study was described to predict multiple intentions simultaneously through a single classification model. Finally, we suggest our view for the future direction of BCI study. Although there are still many limitations when using BCI in daily life, we hope that our studies will be a foundation for developing a practical BCI system

Studies to Overcome Brain–Computer Interface Challenges

A brain–computer interface (BCI) is a promising technology that can analyze brain signals and control a robot or computer according to a user’s intention. This paper introduces our studies to overcome the challenges of using BCIs in daily life. There are several methods to implement BCIs, such as sensorimotor rhythms (SMR), P300, and steady-state visually evoked potential (SSVEP). These methods have different pros and cons according to the BCI type. However, all these methods are limited in choice. Controlling the robot arm according to the intention enables BCI users can do various things. We introduced the study predicting three-dimensional arm movement using a non-invasive method. Moreover, the study was described compensating the prediction using an external camera for high accuracy. For daily use, BCI users should be able to turn on or off the BCI system because of the prediction error. The users should also be able to change the BCI mode to the efficient BCI type. The BCI mode can be transformed based on the user state. Our study was explained estimating a user state based on a brain’s functional connectivity and a convolutional neural network (CNN). Additionally, BCI users should be able to do various tasks, such as carrying an object, walking, or talking simultaneously. A multi-function BCI study was described to predict multiple intentions simultaneously through a single classification model. Finally, we suggest our view for the future direction of BCI study. Although there are still many limitations when using BCI in daily life, we hope that our studies will be a foundation for developing a practical BCI system

Eco-Efficiency of Government Policy and Exports in the Bioenergy Technology Market

This study investigates how the eco-efficiency of government policy—continuously implementing innovation-friendly policy based on both environmental and economic considerations—affects the export performance of bioenergy technologies, using panel data from 16 countries during 1995–2012. Various heterogeneous panel framework tests are conducted. Our panel unit root and co-integration tests, which allow for cross-sectional dependence in the panel, show that the time series data on the eco-efficiency of public support, exports, and gross domestic product (GDP) are integrated and co-integrated. We set up a panel vector error correction model (VECM) to empirically test the casual relationship among the variables examined. The long-term parameters of the variables were calculated using dynamic ordinary lease squares (DOLS). Panel difference generalized method of moments (GMM) estimations were conducted to test the short-term relationship among the variables. The results of this study therefore show that the eco-efficiency of government policy positively influences export performance in the long run, but not in the short run. The presented findings also indicate that efficiently implemented government policy plays a crucial role in achieving environmentally sound and sustainable development, showing path dependence among the eco-efficiency of government policy, exports, and GDP. We finally suggest policy implications based on the results

User-state Prediction using Brain Connectivity

There are different types of brain-computer interfaces (BCIs). The different type of the BCI has different strengths and weaknesses. Therefore, different type BCI is used depending on the applications. The BCI system will be powerful if different type of the BCI can be applied to the one system according to a user-state. To implement the BCI system, prediction of the user state is required. In this paper, we investigated the change of brain networks according to the user states using mutual information. Our results showed that the brain networks were changed according to the user states. The result implies that multi-mode BCI system will be possible by predicting user state using brain connectivity.N

A magnetoencephalography dataset during three-dimensional reaching movements for brain-computer interfaces

Abstract Studying the motor-control mechanisms of the brain is critical in academia and also has practical implications because techniques such as brain-computer interfaces (BCIs) can be developed based on brain mechanisms. Magnetoencephalography (MEG) signals have the highest spatial resolution (~3 mm) and temporal resolution (~1 ms) among the non-invasive methods. Therefore, the MEG is an excellent modality for investigating brain mechanisms. However, publicly available MEG data remains scarce due to expensive MEG equipment, requiring a magnetically shielded room, and high maintenance costs for the helium gas supply. In this study, we share the 306-channel MEG and 3-axis accelerometer signals acquired during three-dimensional reaching movements. Additionally, we provide analysis results and MATLAB codes for time-frequency analysis, F-value time-frequency analysis, and topography analysis. These shared MEG datasets offer valuable resources for investigating brain activities or evaluating the accuracy of prediction algorithms. To the best of our knowledge, this data is the only publicly available MEG data measured during reaching movements

Macroscopic Neural Oscillation during Skilled Reaching Movements in Humans

The neural mechanism of skilled movements, such as reaching, has been considered to differ from that of rhythmic movement such as locomotion. It is generally thought that skilled movements are consciously controlled by the brain, while rhythmic movements are usually controlled autonomously by the spinal cord and brain stem. However, several studies in recent decades have suggested that neural networks in the spinal cord may also be involved in the generation of skilled movements. Moreover, a recent study revealed that neural activities in the motor cortex exhibit rhythmic oscillations corresponding to movement frequency during reaching movements as rhythmic movements. However, whether the oscillations are generated in the spinal cord or the cortical circuit in the motor cortex causes the oscillations is unclear. If the spinal cord is involved in the skilled movements, then similar rhythmic oscillations with time delays should be found in macroscopic neural activity. We measured whole-brain MEG signals during reaching. The MEG signals were analyzed using a dynamical analysis method. We found that rhythmic oscillations with time delays occur in all subjects during reaching movements. The results suggest that the corticospinal system is involved in the generation and control of the skilled movements as rhythmic movements

MEG (raw data)

The 306-channel MEG and 3-axis accelerometer signals were measured during three-dimensional reaching movements. The "Raw data.zip" has continuous MEG signals, after tSSS filtering, and contains 18 files with measurements from nine subjects across two sessions

MEG (epoched data)

The 306-channel MEG and 3-axis accelerometer signals which were measured during three-dimensional reaching movements. The MEG data in the "epoched data.zip" is provided in mat format and compatible with MATLAB software. The epoched data folder includes MEG signals truncated before and after the events. The folder has 18 mat files from nine subjects across two sessions