Most of the recent developments in ultrasensitive detection of nucleic acid are based on the gold nanoparticles and carbon nanotubes as a medium of signal amplification. Here, we present an ...ultrasensitive electrochemical nucleic acid biosensor using the conducting polyaniline (PANI) nanotube array as the signal enhancement element. The PANI nanotube array of a highly organized structure was fabricated under a well-controlled nanoscale dimension on the graphite electrode using a thin nanoporous layer as a template, and 21-mer oligonucleotide probes were immobilized on these PANI nanotubes. In comparison with gold nanoparticle- or carbon nanotube-based DNA biosensors, our PANI nanotube array-based DNA biosensor could achieve similar sensitivity without catalytic enhancement, purification, or end-opening processing. The electrochemical results showed that the conducting PANI nanotube array had a signal enhancement capability, allowing the DNA biosensor to readily detect the target oligonucleotide at a concentration as low as 1.0 fM (∼300 zmol of target molecules). In addition, this biosensor demonstrated good capability of differentiating the perfect matched target oligonucleotide from one-nucleotide mismatched oligonucleotides even at a concentration of 37.59 fM. This detection specificity indicates that this biosensor could be applied to single-nucleotide polymorphism analysis and single-mutation detection.
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•Studied the characteristics and capacity of information transfer in the visual-evoked pathway.•Proposed a broadband white noise BCI to surpass the SSEVP BCI performance record.•Integrate information ...theory and decoding analysis to study general sensory-evoked BCIs.
An essential priority of visual brain-computer interfaces (BCIs) is to enhance the information transfer rate (ITR) to achieve high-speed communication. Despite notable progress, noninvasive visual BCIs have encountered a plateau in ITRs, leaving it uncertain whether higher ITRs are achievable. In this study, we used information theory to study the characteristics and capacity of the visual-evoked channel, which leads us to investigate whether and how we can decode higher information rates in a visual BCI system. Using information theory, we estimate the upper and lower bounds of the information rate with the white noise (WN) stimulus. Consequently, we found out that the information rate is determined by the signal-to-noise ratio (SNR) in the frequency domain, which reflects the spectrum resources of the channel. Based on this discovery, we propose a broadband WN BCI by implementing stimuli on a broader frequency band than the steady-state visual evoked potentials (SSVEPs)-based BCI. Through validation, the broadband BCI outperforms the SSVEP BCI by an impressive 7 bps, setting a record of 50 bps. The integration of information theory and the decoding analysis presented in this study offers valuable insights applicable to general sensory-evoked BCIs, providing a potential direction of next-generation human-machine interaction systems.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Objective: The tradeoff between calibration effort and model performance still hinders the user experience for steady-state visual evoked brain-computer interfaces (SSVEP-BCI). To address this issue ...and improve model generalizability, this work investigated the adaptation from the cross-dataset model to avoid the training process, while maintaining high prediction ability. Methods: When a new subject enrolls, a group of user-independent (UI) models is recommended as the representative from a multi-source data pool. The representative model is then augmented with online adaptation and transfer learning techniques based on user-dependent (UD) data. The proposed method is validated on both offline (N=55) and online (N=12) experiments. Results: Compared with the UD adaptation, the recommended representative model relieved approximately 160 trials of calibration efforts for a new user. In the online experiment, the time window decreased from 2 s to 0.56±0.2 s, while maintaining high prediction accuracy of 0.89-0.96. Finally, the proposed method achieved the average information transfer rate (ITR) of 243.49 bits/min, which is the highest ITR ever reported in a complete calibration-free setting. The results of the offline result were consistent with the online experiment. Conclusion: Representatives can be recommended even in a cross-subject/device/session situation. With the help of represented UI data, the proposed method can achieve sustained high performance without a training process. Significance: This work provides an adaptive approach to the transferable model for SSVEP-BCIs, enabling a more generalized, plug-and-play and high-performance BCI free of calibrations.
Brain-computer interfaces (BCIs) have attracted considerable attention in motor and language rehabilitation. Most devices use cap-based non-invasive, headband-based commercial products or ...microneedle-based invasive approaches, which are constrained for inconvenience, limited applications, inflammation risks and even irreversible damage to soft tissues. Here, we propose in-ear visual and auditory BCIs based on in-ear bioelectronics, named as SpiralE, which can adaptively expand and spiral along the auditory meatus under electrothermal actuation to ensure conformal contact. Participants achieve offline accuracies of 95% in 9-target steady state visual evoked potential (SSVEP) BCI classification and type target phrases successfully in a calibration-free 40-target online SSVEP speller experiment. Interestingly, in-ear SSVEPs exhibit significant 2
harmonic tendencies, indicating that in-ear sensing may be complementary for studying harmonic spatial distributions in SSVEP studies. Moreover, natural speech auditory classification accuracy can reach 84% in cocktail party experiments. The SpiralE provides innovative concepts for designing 3D flexible bioelectronics and assists the development of biomedical engineering and neural monitoring.
Brain-computer interfaces (BCIs) offer a way to interact with computers without relying on physical movements. Non-invasive electroencephalography-based visual BCIs, known for efficient speed and ...calibration ease, face limitations in continuous tasks due to discrete stimulus design and decoding methods. To achieve continuous control, we implemented a novel spatial encoding stimulus paradigm and devised a corresponding projection method to enable continuous modulation of decoded velocity. Subsequently, we conducted experiments involving 17 participants and achieved Fitt’s information transfer rate (ITR) of 0.55 bps for the fixed tracking task and 0.37 bps for the random tracking task. The proposed BCI with a high Fitt’s ITR was then integrated into two applications, including painting and gaming. In conclusion, this study proposed a visual BCI based-control method to go beyond discrete commands, allowing natural continuous control based on neural activity.
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•Advancement in visual BCI toward natural and efficient interaction•Novel spatial encoding stimulus paradigm and decoding methods for continuous control•High performance in visual tracking tasks•Promised real-world applicability
Biological sciences; Applied sciences; Bioengineering
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This study applied a steady-state visual evoked potential (SSVEP) based brain–computer interface (BCI) to a patient in lock-in state with amyotrophic lateral sclerosis (ALS) and validated its ...feasibility for communication. The developed calibration-free and asynchronous spelling system provided a natural and efficient communication experience for the patient, achieving a maximum free-spelling accuracy above 90% and an information transfer rate of over 22.203 bits/min. A set of standard frequency scanning and task spelling data were also acquired to evaluate the patient’s SSVEP response and to facilitate further personalized BCI design. The results demonstrated that the proposed SSVEP-based BCI system was practical and efficient enough to provide daily life communication for ALS patients.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
The thermal stability of polyaniline (PAni) is of great importance in commercial applications. PAni and its emeraldine base form (PAni-EB) were electrochemically synthesized and subjected to ...thermogravimetric analysis (TGA) in inert atmosphere, which illustrated that the dopant HClO
4 could act as the oxidizing agent at elevated temperature and caused serious decomposition of PAni. Combined with differential scanning calorimetry (DSC),
in-situ X-ray diffraction (XRD) and Fourier transform infrared spectroscopy (FT-IR) analyses, the crosslinking reaction among PAni-EB molecules at about 250
°C was characterized, and the kinetic parameters were calculated by applying different heating rates in the DSC measurement.
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A method of preparing continuous（Al＋Al2O3）-coated SiC fiber reinforced nickel matrix composite was presented,in which the diffusion between SiC fiber and nickel matrix could be prevented.Magnetron ...sputtering is used to deposit Ni coating on the surface of the（Al＋Al2O3）-coated SiC fiber in preparation of the precursor wires.It is shown that the deposited Ni coating combines well with the（Al＋Al2O3） coating and has little negative effect on the tensile strength of（Al＋Al2O3）-coated SiC fiber.Solid-state diffusion bonding process is employed to prepare the（Al＋Al2O3）-coated SiC fiber reinforced nickel matrix with 37% fibers in volume.The solid-state diffusion bonding process is optimized and the optimum parameters are temperature of 870,pressure of 50 MPa and holding time of 2 h.Under this condition,the precursor wires can diffuse well,composite of full density can be formed and the（Al＋Al2O3） coating is effective to restrict the reaction between SiC fiber and nickel matrix.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
A brain-computer interface (BCI) provides a direct communication channel between a brain and an external device. Steady-state visual evoked potential based BCI (SSVEP-BCI) has received increasing ...attention due to its high information transfer rate, which is accomplished by individual calibration for frequency recognition. Task-related component analysis (TRCA) is a recent and state-of-the-art method for individually calibrated SSVEP-BCIs. However, in TRCA, the spatial filter learned from each stimulus may be redundant and temporal information is not fully utilized. To address this issue, this paper proposes a novel method, i.e., task-discriminant component analysis (TDCA), to further improve the performance of individually calibrated SSVEP-BCI. The performance of TDCA was evaluated by two publicly available benchmark datasets, and the results demonstrated that TDCA outperformed ensemble TRCA and other competing methods by a significant margin. An offline and online experiment testing 12 subjects further validated the effectiveness of TDCA. The present study provides a new perspective for designing decoding methods in individually calibrated SSVEP-BCI and presents insight for its implementation in high-speed brain speller applications.
The ultimate goal of brain-computer interfaces (BCIs) based on visual modulation paradigms is to achieve high-speed performance without the burden of extensive calibration. Code-modulated visual ...evoked potential-based BCIs (c-VEP-BCIs) modulated by broadband white noise (WN) offer various advantages, including increased communication speed, expanded encoding target capabilities, and enhanced coding flexibility. However, the complexity of the spatial-temporal patterns under broadband stimuli necessitates extensive calibration for effective target identification in c-VEP-BCIs. Consequently, the information transfer rate (ITR) of c-VEP-BCI under limited calibration usually stays around 100 bits per minute (bpm), significantly lagging behind state-of-the-art steady-state visual evoked potential-based BCIs (SSVEP-BCIs), which achieve rates above 200 bpm. To enhance the performance of c-VEP-BCIs with minimal calibration, we devised an efficient calibration stage involving a brief single-target flickering, lasting less than a minute, to extract generalizable spatial-temporal patterns. Leveraging the calibration data, we developed two complementary methods to construct c-VEP temporal patterns: the linear modeling method based on the stimulus sequence and the transfer learning techniques using cross-subject data. As a result, we achieved the highest ITR of 250 bpm under a minute of calibration, which has been shown to be comparable to the state-of-the-art SSVEP paradigms. In summary, our work significantly improved the c-VEP performance under few-shot learning, which is expected to expand the practicality and usability of c-VEP-BCIs.
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