Controlling a Mouse Pointer with a Single-Channel EEG Sensor

(1) Goals: The purpose of this study was to analyze the feasibility of using the information obtained from a one-channel electro-encephalography (EEG) signal to control a mouse pointer. We used a low-cost headset, with one dry sensor placed at the FP1 position, to steer a mouse pointer and make selections through a combination of the user’s attention level with the detection of voluntary blinks. There are two types of cursor movements: spinning and linear displacement. A sequence of blinks allows for switching between these movement types, while the attention level modulates the cursor’s speed. The influence of the attention level on performance was studied. Additionally, Fitts’ model and the evolution of the emotional states of participants, among other trajectory indicators, were analyzed. (2) Methods: Twenty participants distributed into two groups (Attention and No-Attention) performed three runs, on different days, in which 40 targets had to be reached and selected. Target positions and distances from the cursor’s initial position were chosen, providing eight different indices of difficulty (IDs). A self-assessment manikin (SAM) test and a final survey provided information about the system’s usability and the emotions of participants during the experiment. (3) Results: The performance was similar to some brain–computer interface (BCI) solutions found in the literature, with an averaged information transfer rate (ITR) of 7 bits/min. Concerning the cursor navigation, some trajectory indicators showed our proposed approach to be as good as common pointing devices, such as joysticks, trackballs, and so on. Only one of the 20 participants reported difficulty in managing the cursor and, according to the tests, most of them assessed the experience positively. Movement times and hit rates were significantly better for participants belonging to the attention group. (4) Conclusions: The proposed approach is a feasible low-cost solution to manage a mouse pointer.

Solving the SSVEP Paradigm Using the Nonlinear Canonical Correlation Analysis Approach

This paper presents the implementation of nonlinear canonical correlation analysis (NLCCA) approach to detect steady-state visual evoked potentials (SSVEP) quickly. The need for the fast recognition of proper stimulus to help end an SSVEP task in a BCI system is justified due to the flickering external stimulus exposure that causes users to start to feel fatigued. Measuring the accuracy and exposure time can be carried out through the information transfer rate—ITR, which is defined as a relationship between the precision, the number of stimuli, and the required time to obtain a result. NLCCA performance was evaluated by comparing it with two other approaches—the well-known canonical correlation analysis (CCA) and the least absolute reduction and selection operator (LASSO), both commonly used to solve the SSVEP paradigm. First, the best average ITR value was found from a dataset comprising ten healthy users with an average age of 28, where an exposure time of one second was obtained. In addition, the time sliding window responses were observed immediately after and around 200 ms after the flickering exposure to obtain the phase effects through the coefficient of variation (CV), where NLCCA obtained the lowest value. Finally, in order to obtain statistical significance to demonstrate that all approaches differ, the accuracy and ITR from the time sliding window responses was compared using a statistical analysis of variance per approach to identify differences between them using Tukey’s test.

A Hybrid Brain–Computer Interface for Real-Life Meal-Assist Robot Control

Assistant devices such as meal-assist robots aid individuals with disabilities and support the elderly in performing daily activities. However, existing meal-assist robots are inconvenient to operate due to non-intuitive user interfaces, requiring additional time and effort. Thus, we developed a hybrid brain–computer interface-based meal-assist robot system following three features that can be measured using scalp electrodes for electroencephalography. The following three procedures comprise a single meal cycle. (1) Triple eye-blinks (EBs) from the prefrontal channel were treated as activation for initiating the cycle. (2) Steady-state visual evoked potentials (SSVEPs) from occipital channels were used to select the food per the user’s intention. (3) Electromyograms (EMGs) were recorded from temporal channels as the users chewed the food to mark the end of a cycle and indicate readiness for starting the following meal. The accuracy, information transfer rate, and false positive rate during experiments on five subjects were as follows: accuracy (EBs/SSVEPs/EMGs) (%): (94.67/83.33/97.33); FPR (EBs/EMGs) (times/min): (0.11/0.08); ITR (SSVEPs) (bit/min): 20.41. These results revealed the feasibility of this assistive system. The proposed system allows users to eat on their own more naturally. Furthermore, it can increase the self-esteem of disabled and elderly peeople and enhance their quality of life.

Neurofeedback training of control network improves SSVEP-based BCI performance in children

Background: In the past 20 years, neural engineering has made unprecedented progress in the interpretation of brain information (e.g., brain-computer interfaces) and neuromodulation (e.g., electromagnetic stimulation and neurofeedback). However, the study of improving the performance of the brain-computer interface (BCI) using the neuromodulation method rarely exists. The present study designs a neurofeedback training method to improve the performance of steady-state visual evoked potential (SSVEP) BCI and further explores its underlying mechanisms. Methods: As the parietal lobe is the sole hub of information transmission, up-regulating alpha-band power of the parietal lobe by neurofeedback training was presented in this study as a new neural modulation method to improve SSVEP-based BCI in this study. Results: After this neurofeedback training (NFT), the signal-to-noise ratio (SNR), accuracy, and information transfer rate (ITR) of SSVEP-based BCI were increased by 5.8%, 4.7%, and 15.6% respectively. However, no improvement has been observed in the control group in which the subjects do not participate in NFT. Evidence from the network analysis and attention test further indicate that NFT improves attention via developing the control ability of the parietal lobe and then enhances the above SSVEP indicators.Conclusion: Up-regulating parietal alpha-amplitude using neurofeedback training significantly improves the SSVEP-baesd BCI performance through modulating the control network. The study validates an effective neuromodulation method, and possibly also contributes to explaining the function of the parietal lobe in the control network.

Towards the non-zero field cesium magnetic sensor array for magnetoencephalography

Magnetic sensors developed for application in magnetoencephalography must meet a number of requirements; the main ones are compactness, sensitivity and response speed. We present a quantum optically pumped atomic sensor with cell volume of 0.5cm³ that meets these requirements and is operable in nonzero magnetic fields. The ultimate sensitivity of the sensor was estimated as (using the criteria of the ratio of the slope of the magnetic resonance signal to the shot noise spectral density) to be better than 5 fT/Hz^1/2. The actual sensitivity, measured in a gradiometric scheme, reaches 13 fT/Hz^1/2per sensor. We also present a novel and fast algorithm for optimization of the geometric properties of non-zero field sensor array with respect to maximization of the information transfer rate for cortical sources.<br>

Towards the non-zero field cesium magnetic sensor array for magnetoencephalography

Magnetic sensors developed for application in magnetoencephalography must meet a number of requirements; the main ones are compactness, sensitivity and response speed. We present a quantum optically pumped atomic sensor with cell volume of 0.5cm³ that meets these requirements and is operable in nonzero magnetic fields. The ultimate sensitivity of the sensor was estimated as (using the criteria of the ratio of the slope of the magnetic resonance signal to the shot noise spectral density) to be better than 5 fT/Hz^1/2. The actual sensitivity, measured in a gradiometric scheme, reaches 13 fT/Hz^1/2per sensor. We also present a novel and fast algorithm for optimization of the geometric properties of non-zero field sensor array with respect to maximization of the information transfer rate for cortical sources.<br>

An Open Dataset for Wearable SSVEP-Based Brain-Computer Interfaces

Brain-computer interfaces (BCIs) provide humans a new communication channel by encoding and decoding brain activities. Steady-state visual evoked potential (SSVEP)-based BCI stands out among many BCI paradigms because of its non-invasiveness, little user training, and high information transfer rate (ITR). However, the use of conductive gel and bulky hardware in the traditional Electroencephalogram (EEG) method hinder the application of SSVEP-based BCIs. Besides, continuous visual stimulation in long time use will lead to visual fatigue and pose a new challenge to the practical application. This study provides an open dataset, which is collected based on a wearable SSVEP-based BCI system, and comprehensively compares the SSVEP data obtained by wet and dry electrodes. The dataset consists of 8-channel EEG data from 102 healthy subjects performing a 12-target SSVEP-based BCI task. For each subject, 10 consecutive blocks were recorded using wet and dry electrodes, respectively. The dataset can be used to investigate the performance of wet and dry electrodes in SSVEP-based BCIs. Besides, the dataset provides sufficient data for developing new target identification algorithms to improve the performance of wearable SSVEP-based BCIs.

Soliton orthogonal frequency division multiplexing with phase–frequency coding on the base of inverse scattering transform

We propose a method for increasing the speed and spectral efficiency of information transmission over soliton communication lines, based on algorithms for solving inverse and direct scattering problems, in the frame of a modern soliton orthogonal frequency division multiplexing (SOFDM) approach. The proposed method uses a simultaneous phase and frequency coding. This phase–frequency coding method retains all the technological advantages of the SOFDM method, and due to the additional frequency coding, a noticeable increase in the information transfer rate over the soliton optical lines is expected. Numerical modeling confirmed the proposed method for telecommunication applications’ prospects.