Using the Cocktail Party Effect to Add the Coding Dimension of Auditory Event Related Potential Brain-Computer Interface.

OBJECTIVE: The auditory event-related potential based brain-computer interface (aERP-BCI) is a classical paradigm of brain-computer communication. To improve the coding efficiency of aERP-BCI, this study proposes a method using two parallel voice channels to add the coding dimension based on the cocktail party effect. METHODS: The novel paradigm used male and female voices to establish two parallel oddball sound stimulus sequences. In comparison, the baseline paradigm only presented male or female stimulus sequences. Both the double voice condition (DVC) and the single voice condition (SVC) paradigms carried out offline experiments and the DVC also carried out online experiment. Subsequently, the EEG signal and BCI operation results were compared and analyzed. CONCLUSION: The cocktail party effect caused a significant difference in the EEG responses of non-target stimulus between the focused vocal channel and the ignored vocal channel under the DVC paradigm, and the focused and ignored channels achieved a recognition accuracy of 97.2%. The target recognition rate of DVC was 82.3%, with no significant difference compared with 85% of SVC while the information transfer rate (ITR) of DVC reaching 15.3 bits/min was significantly higher than that of SVC. SIGNIFICANCE: The cocktail party effect improves the coding efficiency by adding parallel channels without reducing the target/non-target stimulus recognition in the focused vocal channel. This provides a novel direction for the performance improvement of aERP-BCI.

MetaBCI: An open-source platform for brain-computer interfaces.

BACKGROUND: Recently, brain-computer interfaces (BCIs) have attracted worldwide attention for their great potential in clinical and real-life applications. To implement a complete BCI system, one must set up several links to translate the brain intent into computer commands. However, there is not an open-source software platform that can cover all links of the BCI chain. METHOD: This study developed a one-stop open-source BCI software, namely MetaBCI, to facilitate the construction of a BCI system. MetaBCI is written in Python, and has the functions of stimulus presentation (Brainstim), data loading and processing (Brainda), and online information flow (Brainflow). This paper introduces the detailed information of MetaBCI and presents four typical application cases. RESULTS: The results showed that MetaBCI was an extensible and feature-rich software platform for BCI research and application, which could effectively encode, decode, and feedback brain activities. CONCLUSIONS: MetaBCI can greatly lower the BCI's technical threshold for BCI beginners and can save time and cost to build up a practical BCI system. The source code is available at https://github.com/TBC-TJU/MetaBCI, expecting new contributions from the BCI community.

Phase-Field Study of Exchange Coupling in Co-Pt Nonstandard Nanochessboards.

The Co-Pt binary system can form a two-phase nanochessboard structure comprising regularly aligned nanorods of magnetically hard tetragonal L10 phase and magnetically soft cubic L12 phase. This Co-Pt nanochessboard, being an exchange-coupled magnetic nanocomposite, exhibits a strong effect on magnetic domains and coercivity. While the ideal nanochessboard structure has tiles with equal edge lengths (a = b), the non-ideal or nonstandard nanochessboard structure has tiles with unequal edge lengths (a ≠ b). In this study, we employed phase-field modeling and computer simulation to systematically investigate the exchange coupling effect on magnetic properties in nonstandard nanochessboards. The simulations reveal that coercivity is dependent on the length scale, with magnetic hardening occurring below the critical exchange length, followed by magnetic softening above the critical exchange length, similar to the standard nanochessboards. Moreover, the presence of unequal edge lengths induces an anisotropic exchange coupling and shifts the coercivity peak with the length scale.

Basic Graphic Shape Decoding for EEG-based Brain-Computer Interfaces.

Image decoding using electroencephalogram (EEG) has became a new topic for brain-computer interface (BCI) studies in recent years. Previous studies often tried to decode EEG signals modulated by a picture of complex object. However, it's still unclear how a simple image with different positions and orientations influence the EEG signals. To this end, this study used a same white bar with eight different spatial patterns as visual stimuli. Convolutional neural network (CNN) combined with long short-term memory (LSTM) was employed to decode the corresponding EEG signals. Four subjects were recruited in this study. As a result, the highest binary classification accuracy could reach 97.2%, 95.7%, 90.2%, and 88.3% for the four subjects, respectively. Almost all subjects could achieve more than 70% for 4-class classification. The results demonstrate basic graphic shapes are decodable from EEG signals, which hold promise for image decoding of EEG-based BCIs.

Optimization of Task Allocation for Collaborative Brain-Computer Interface Based on Motor Imagery.

OBJECTIVE: Collaborative brain-computer interfaces (cBCIs) can make the BCI output more credible by jointly decoding concurrent brain signals from multiple collaborators. Current cBCI systems usually require all collaborators to execute the same mental tasks (common-work strategy). However, it is still unclear whether the system performance will be improved by assigning different tasks to collaborators (division-of-work strategy) while keeping the total tasks unchanged. Therefore, we studied a task allocation scheme of division-of-work and compared the corresponding classification accuracies with common-work strategy's. APPROACH: This study developed an electroencephalograph (EEG)-based cBCI which had six instructions related to six different motor imagery tasks (MI-cBCI), respectively. For the common-work strategy, all five subjects as a group had the same whole instruction set and they were required to conduct the same instruction at a time. For the division-of-work strategy, every subject's instruction set was a subset of the whole one and different from each other. However, their union set was equal to the whole set. Based on the number of instructions in a subset, we divided the division-of-work strategy into four types, called "2 Tasks" … "5 Tasks." To verify the effectiveness of these strategies, we employed EEG data collected from 19 subjects who independently performed six types of MI tasks to conduct the pseudo-online classification of MI-cBCI. MAIN RESULTS: Taking the number of tasks performed by one collaborator as the horizontal axis (two to six), the classification accuracy curve of MI-cBCI was mountain-like. The curve reached its peak at "4 Tasks," which means each subset contained four instructions. It outperformed the common-work strategy ("6 Tasks") in classification accuracy (72.29 ± 4.43 vs. 58.53 ± 4.36%). SIGNIFICANCE: The results demonstrate that our proposed task allocation strategy effectively enhanced the cBCI classification performance and reduced the individual workload.

'Write' but not 'spell' Chinese characters with a BCI-controlled robot.

Visual brain-computer interface (BCI) systems have made tremendous process in recent years. It has been demonstrated to perform well in spelling words. However, different from spelling English words in one-dimension sequences, Chinese characters are often written in a two-dimensional structure. Previous studies had never investigated how to use BCI to 'write' but not 'spell' Chinese characters. This study developed an innovative BCI-controlled robot for writing Chinese characters. The BCI system contained 108 commands displayed in a 9*12 array. A pixel-based writing method was proposed to map the starting point and ending point of each stroke of Chinese characters to the array. Connecting the starting and ending points for each stroke can make up any Chinese character. The large command set was encoded by the hybrid P300 and SSVEP features efficiently, in which each output needed only 1s of EEG data. The task-related component analysis was used to decode the combined features. Five subjects participated in this study and achieved an average accuracy of 87.23% and a maximal accuracy of 100%. The corresponding information transfer rate was 56.85 bits/min and 71.10 bits/min, respectively. The BCI-controlled robotic arm could write a Chinese character '' with 16 strokes within 5.7 seconds for the best subject. The demo video can be found at https://www.youtube.com/watch?v=A1w-e2dBGl0. The study results demonstrated that the proposed BCI-controlled robot is efficient for writing ideogram (e.g. Chinese characters) and phonogram (e.g. English letter), leading to broad prospects for real-world applications of BCIs.

Optimizing SSVEP-Based BCI System towards Practical High-Speed Spelling.

The brain-computer interface (BCI) spellers based on steady-state visual evoked potentials (SSVEPs) have recently been widely investigated for their high information transfer rates (ITRs). This paper aims to improve the practicability of the SSVEP-BCIs for high-speed spelling. The system acquired the electroencephalogram (EEG) data from a self-developed dedicated EEG device and the stimulation was arranged as a keyboard. The task-related component analysis (TRCA) spatial filter was modified (mTRCA) for target classification and showed significantly higher performance compared with the original TRCA in the offline analysis. In the online system, the dynamic stopping (DS) strategy based on Bayesian posterior probability was utilized to realize alterable stimulating time. In addition, the temporal filtering process and the programs were optimized to facilitate the online DS operation. Notably, the online ITR reached 330.4 ± 45.4 bits/min on average, which is significantly higher than that of fixed stopping (FS) strategy, and the peak value of 420.2 bits/min is the highest online spelling ITR with a SSVEP-BCI up to now. The proposed system with portable EEG acquisition, friendly interaction, and alterable time of command output provides more flexibility for SSVEP-based BCIs and is promising for practical high-speed spelling.

A Multifocal SSVEPs-based Brain-Computer Interface with Less Calibration Time.

For the past few years, electroencephalogram (EEG)-based brain-computer interfaces (BCIs) have gotten tremendous progress and attracted increasing attention. To broaden the application of BCIs, researchers have focused on the increasement of the BCI instruction number in recent years. However, with a large number of instructions, the BCI calibration time will be too long to be accepted in practical usage. This study proposed a new coding method based on multifocal steady-state visual evoked potentials (mfSSVEPs), in which 16 targets were binary coded by 4 frequencies. Notably, the training data needed for calibration corresponded to only five out of the sixteen targets. Five volunteers were recruited to test this paradigm. Task-related component analysis combined with a probabilistic model were employed for target recognition. As a result, the accuracy could reach as high as 93.1% with 1s-length data. The highest information transfer rate (ITR) was 101.1 bits/min with an average of 73.9 bits/min. The results indicate that this new paradigm is promising to encode a large BCI instruction set with less trainings.

Does A Subject Independent Dynamic Stopping Model for P300 Speller Work on Different Flash Durations and Inter Stimulus Intervals?

Event-related potential (ERP)-based brain- computer interfacing (BCI) is an effective communication method. However, calibration itself can be unintuitive and tedious for users. The no-calibration Subject Independent Brain Computer Interface (SIBCI) is a popular solution to the lengthy calibration. Researches have proved the subject independent model is efficient in some P300 spellers, but it is still need to be explored whether the subject independent model works when the flash durations (FDs) and the inter stimulus intervals (ISIs) are changed in a P300 speller. This study introduces a subject independent dynamical stopping model (SIDSM), which based on a subject independent model to dynamically stop the data collection process. The performance of the SIDSM is studied by modifying the FDs and ISIs in online experiments for 8 subjects. Results showed the SIDSM has an average accuracy of 92.45% for different settings. This research proved that the SIDSM is very robust to different stimulus parameters as good performance is observed across all experimental sessions.

Effects of Distracting Task with Different Mental Workload on Steady-State Visual Evoked Potential Based Brain Computer Interfaces-an Offline Study.

Brain-computer interfaces (BCIs), independent of the brain's normal output pathways, are attracting an increasing amount of attention as devices that extract neural information. As a typical type of BCI system, the steady-state visual evoked potential (SSVEP)-based BCIs possess a high signal-to-noise ratio and information transfer rate. However, the current high speed SSVEP-BCIs were implemented with subjects concentrating on stimuli, and intentionally avoided additional tasks as distractors. This paper aimed to investigate how a distracting simultaneous task, a verbal n-back task with different mental workload, would affect the performance of SSVEP-BCI. The results from fifteen subjects revealed that the recognition accuracy of SSVEP-BCI was significantly impaired by the distracting task, especially under a high mental workload. The average classification accuracy across all subjects dropped by 8.67% at most from 1- to 4-back, and there was a significant negative correlation (maximum r = -0.48, p < 0.001) between accuracy and subjective mental workload evaluation of the distracting task. This study suggests a potential hindrance for the SSVEP-BCI daily use, and then improvements should be investigated in the future studies.

Effects of Temporal Congruity Between Auditory and Visual Stimuli Using Rapid Audio-Visual Serial Presentation.

GOAL: Combining visual and auditory stimuli in event-related potential (ERP)-based spellers gained more attention in recent years. Few of these studies notice the difference of ERP components and system efficiency caused by the shifting of visual and auditory onset. Here, we aim to study the effect of temporal congruity of auditory and visual stimuli onset on bimodal brain-computer interface (BCI) speller. METHODS: We designed five visual and auditory combined paradigms with different visual-to-auditory delays (-33 to +100 ms). Eleven participants attended in this study. ERPs were acquired and aligned according to visual and auditory stimuli onset, respectively. ERPs of Fz, Cz, and PO7 channels were studied through the statistical analysis of different conditions both from visual-aligned ERPs and audio-aligned ERPs. Based on the visual-aligned ERPs, classification accuracy was also analyzed to seek the effects of visual-to-auditory delays. RESULTS: The latencies of ERP components depended mainly on the visual stimuli onset. Auditory stimuli onsets influenced mainly on early component accuracies, whereas visual stimuli onset determined later component accuracies. The latter, however, played a dominate role in overall classification. SIGNIFICANCE: This study is important for further studies to achieve better explanations and ultimately determine the way to optimize the bimodal BCI application.

Differential effects of variable frequencies of manual acupuncture at ST36 in rats with atropine-induced inhibition of gastric motility.

OBJECTIVE: The 'intensity-response' relationship between acupuncture stimulation and therapeutic effect is currently the focus of much research interest. The same needling manipulation with different frequencies can generate differential levels of stimulus. This study aimed to examine the effects on gastric motility induced by four twirling frequencies based on relatively standardised manual acupuncture (MA) manipulations. METHODS: Twirling manipulations at 1, 2, 3, and 4 Hz were practised before the experiments by a single operator using an MA parameter measurement device and stability was evaluated through time-frequency analysis. Forty-eight Sprague-Dawley rats were randomly divided into six groups (n=8 each): Control, Model, Model+MA (1, 2, 3, and 4 Hz). Rats in the five Model groups received injections of atropine into the tail vein to inhibit gastric motility, which was continuously recorded by a balloon in the gastric antrum. Rats in the four Model+MA groups received MA at 1, 2, 3 and 4 Hz, respectively, for 70 s and needles were retained for a further 5 min. RESULTS: The amplitude of waveforms produced by the four twirling frequencies was relatively consistent and reproducible. The gastric motility amplitude in all groups decreased after modelling (injections of atropine) (p<0.01). Twirling manipulation at 1, 2, and 3 Hz (but not 4 Hz) increased gastric motility amplitude (p<0.05). The increase in gastric motility amplitude induced by MA at 2 Hz was greater than for all other frequencies (p<0.05). CONCLUSIONS: Acupuncture at ST36 helped recover gastric motility amplitude in rats with atropine-induced gastric inhibition and the effects induced by 1-3 Hz frequency were greater than those induced by 4 Hz.

A visual parallel-BCI speller based on the time-frequency coding strategy.

OBJECTIVE: Spelling is one of the most important issues in brain-computer interface (BCI) research. This paper is to develop a visual parallel-BCI speller system based on the time-frequency coding strategy in which the sub-speller switching among four simultaneously presented sub-spellers and the character selection are identified in a parallel mode. APPROACH: The parallel-BCI speller was constituted by four independent P300+SSVEP-B (P300 plus SSVEP blocking) spellers with different flicker frequencies, thereby all characters had a specific time-frequency code. To verify its effectiveness, 11 subjects were involved in the offline and online spellings. A classification strategy was designed to recognize the target character through jointly using the canonical correlation analysis and stepwise linear discriminant analysis. MAIN RESULTS: Online spellings showed that the proposed parallel-BCI speller had a high performance, reaching the highest information transfer rate of 67.4 bit min(-1), with an average of 54.0 bit min(-1) and 43.0 bit min(-1) in the three rounds and five rounds, respectively. SIGNIFICANCE: The results indicated that the proposed parallel-BCI could be effectively controlled by users with attention shifting fluently among the sub-spellers, and highly improved the BCI spelling performance.

Intelligent algorithm tuning PID method of function electrical stimulation using knee joint angle.

Functional electrical stimulation (FES) could restore motor functions for individuals with spinal cord injury (SCI). By applying electric current pulses, FES system could produce muscle contractions, generate joint torques, and thus, achieve joint movements automatically. Since the muscle system is highly nonlinear and time-varying, feedback control is quite necessary for precision control of the preset action. In the present study, we applied two methods (Proportional Integral Derivative (PID) controller based on Back Propagation (BP) neural network and that based on Genetic Algorithm (GA)), to control the knee joint angle for the FES system, while the traditional Ziegler-Nichols method was used in the control group for comparison. They were tested using a muscle model of the quadriceps. The results showed that intelligent algorithm tuning PID controller displayed superior performance than classic Ziegler-Nichols method with constant parameters. More particularly, PID controller tuned by BP neural network was superior on controlling precision to make the feedback signal track the desired trajectory whose error was less than 1.2°±0.16°, while GA-PID controller, seeking the optimal parameters from multipoint simultaneity, resulted in shortened delay in the response. Both strategies showed promise in application of intelligent algorithm tuning PID methods in FES system.