Biosensor Technologies for Augmented Brain–Computer Interfaces in the Next Decades

Lance B.J., Kerick S.E., Ries A.J., Oie K.S., McDowell K.

As the proliferation of technology dramatically infiltrates all aspects of modern life, in many ways the world is becoming so dynamic and complex that technological capabilities are overwhelming human capabilities to optimally interact with and leverage those technologies. Fortunately, these technological advancements have also driven an explosion of neuroscience research over the past several decades, presenting engineers with a remarkable opportunity to design and develop flexible and adaptive brain-based neurotechnologies that integrate with and capitalize on human capabilities and limitations to improve human-system interactions. Major forerunners of this conception are brain-computer interfaces (BCIs), which to this point have been largely focused on improving the quality of life for particular clinical populations and include, for example, applications for advanced communications with paralyzed or “locked in” patients as well as the direct control of prostheses and wheelchairs. Near-term applications are envisioned that are primarily task oriented and are targeted to avoid the most difficult obstacles to development. In the farther term, a holistic approach to BCIs will enable a broad range of task-oriented and opportunistic applications by leveraging pervasive technologies and advanced analytical approaches to sense and merge critical brain, behavioral, task, and environmental information. Communications and other applications that are envisioned to be broadly impacted by BCIs are highlighted; however, these represent just a small sample of the potential of these technologies.

Makeig S., Kothe C., Mullen T., Bigdely-Shamlo N., Zhilin Zhang, Kreutz-Delgado K.

Because of the increasing portability and wearability of noninvasive electrophysiological systems that record and process electrical signals from the human brain, automated systems for assessing changes in user cognitive state, intent, and response to events are of increasing interest. Brain-computer interface (BCI) systems can make use of such knowledge to deliver relevant feedback to the user or to an observer, or within a human-machine system to increase safety and enhance overall performance. Building robust and useful BCI models from accumulated biological knowledge and available data is a major challenge, as are technical problems associated with incorporating multimodal physiological, behavioral, and contextual data that may in the future be increasingly ubiquitous. While performance of current BCI modeling methods is slowly increasing, current performance levels do not yet support widespread uses. Here we discuss the current neuroscientific questions and data processing challenges facing BCI designers and outline some promising current and future directions to address them.

Liao L., Chen C., Wang I., Chen S., Li S., Chen B., Chang J., Lin C.

A brain-computer interface (BCI) is a communication system that can help users interact with the outside environment by translating brain signals into machine commands. The use of electroencephalographic (EEG) signals has become the most common approach for a BCI because of their usability and strong reliability. Many EEG-based BCI devices have been developed with traditional wet- or micro-electro-mechanical-system (MEMS)-type EEG sensors. However, those traditional sensors have uncomfortable disadvantage and require conductive gel and skin preparation on the part of the user. Therefore, acquiring the EEG signals in a comfortable and convenient manner is an important factor that should be incorporated into a novel BCI device. In the present study, a wearable, wireless and portable EEG-based BCI device with dry foam-based EEG sensors was developed and was demonstrated using a gaming control application. The dry EEG sensors operated without conductive gel; however, they were able to provide good conductivity and were able to acquire EEG signals effectively by adapting to irregular skin surfaces and by maintaining proper skin-sensor impedance on the forehead site. We have also demonstrated a real-time cognitive stage detection application of gaming control using the proposed portable device. The results of the present study indicate that using this portable EEG-based BCI device to conveniently and effectively control the outside world provides an approach for researching rehabilitation engineering.

Allen J.L., Jow T.R., Wolfenstine J.

Fe-substituted LiCoPO4 exhibits greatly improved cycle life relative to LiCoPO4. Whereas, pure LiCoPO4 loses more than half of its discharge capacity at the 10th cycle, the Fe-substituted LiCoPO4 retains about 100% of its discharge capacity at the 10th cycle and about 80% of its capacity at the 500th cycle. It is suggested that improved cycle life results from Fe3+ substitution on the Li and Co sites. The partial substitution of Li+ by Fe3+ and Co2+ by Fe2+ and Fe3+ was evidenced from Rietveld analysis of X-ray powder diffraction data, infrared spectroscopy, X-ray photoelectron spectroscopy and Mossbauer spectroscopy. The majority of the Fe3+ substitutes at the Co2+ site. The composition of Fe-substituted LiCoPO4 is Li0.92Co0.8Fe2+0.12Fe3+0.08PO4 for a sample of starting composition LiCo0.8Fe0.2PO4.

Kim D., Lu N., Ma R., Kim Y., Kim R., Wang S., Wu J., Won S.M., Tao H., Islam A., Yu K.J., Kim T., Chowdhury R., Ying M., Xu L., et. al.

Electronic systems with physical properties matched to the human epidermis can be used in clinical monitoring. We report classes of electronic systems that achieve thicknesses, effective elastic moduli, bending stiffnesses, and areal mass densities matched to the epidermis. Unlike traditional wafer-based technologies, laminating such devices onto the skin leads to conformal contact and adequate adhesion based on van der Waals interactions alone, in a manner that is mechanically invisible to the user. We describe systems incorporating electrophysiological, temperature, and strain sensors, as well as transistors, light-emitting diodes, photodetectors, radio frequency inductors, capacitors, oscillators, and rectifying diodes. Solar cells and wireless coils provide options for power supply. We used this type of technology to measure electrical activity produced by the heart, brain, and skeletal muscles and show that the resulting data contain sufficient information for an unusual type of computer game controller.

Liao L., Wang I., Chen S., Chang J., Lin C.

In the present study, novel dry-contact sensors for measuring electro-encephalography (EEG) signals without any skin preparation are designed, fabricated by an injection molding manufacturing process and experimentally validated. Conventional wet electrodes are commonly used to measure EEG signals; they provide excellent EEG signals subject to proper skin preparation and conductive gel application. However, a series of skin preparation procedures for applying the wet electrodes is always required and usually creates trouble for users. To overcome these drawbacks, novel dry-contact EEG sensors were proposed for potential operation in the presence or absence of hair and without any skin preparation or conductive gel usage. The dry EEG sensors were designed to contact the scalp surface with 17 spring contact probes. Each probe was designed to include a probe head, plunger, spring, and barrel. The 17 probes were inserted into a flexible substrate using a one-time forming process via an established injection molding procedure. With these 17 spring contact probes, the flexible substrate allows for high geometric conformity between the sensor and the irregular scalp surface to maintain low skin-sensor interface impedance. Additionally, the flexible substrate also initiates a sensor buffer effect, eliminating pain when force is applied. The proposed dry EEG sensor was reliable in measuring EEG signals without any skin preparation or conductive gel usage, as compared with the conventional wet electrodes.

Chin-Teng Lin, Lun-De Liao, Yu-Hang Liu, I-Jan Wang, Bor-Shyh Lin, Jyh-Yeong Chang

A novel dry foam-based electrode for long-term EEG measurement was proposed in this study. In general, the conventional wet electrodes are most frequently used for EEG measurement. However, they require skin preparation and conduction gels to reduce the skin-electrode contact impedance. The aforementioned procedures when wet electrodes were used usually make trouble to users easily. In order to overcome the aforesaid issues, a novel dry foam electrode, fabricated by electrically conductive polymer foam covered by a conductive fabric, was proposed. By using conductive fabric, which provides partly polarizable electric characteristic, our dry foam electrode exhibits both polarization and conductivity, and can be used to measure biopotentials without skin preparation and conduction gel. In addition, the foam substrate of our dry electrode allows a high geometric conformity between the electrode and irregular scalp surface to maintain low skin-electrode interface impedance, even under motion. The experimental results presented that the dry foam electrode performs better for long-term EEG measurement, and is practicable for daily life applications.

Le L.T., Ervin M.H., Qiu H., Fuchs B.E., Lee W.Y.

Graphene oxide nanosheets, stably dispersed in water at 0.2 wt.%, were inkjet-printed onto Ti foils and thermally reduced at 200 °C in N 2 , as a new method of fabricating inkjet printed graphene electrodes (IPGEs) for supercapacitors. The specific capacitance of IPGE ranged from 48 to 132 F/g, depending on the potential scan rate from 0.5 to 0.01 V/s using 1M H 2 SO 4 as the electrolyte. The initial performance of IPGEs compares favorably to that reported for graphene electrodes prepared by other fabrication methods. This new finding is expected to be particularly useful for designing and fabricating inter-digitized electrode arrays with a lateral spatial resolution of ~ 50 μm for flexible micro-supercapacitors.

Wang Y., Wang Y., Jung T.

Moving a brain-computer interface (BCI) system from a laboratory demonstration to real-life applications still poses severe challenges to the BCI community. This study aims to integrate a mobile and wireless electroencephalogram (EEG) system and a signal-processing platform based on a cell phone into a truly wearable and wireless online BCI. Its practicality and implications in a routine BCI are demonstrated through the realization and testing of a steady-state visual evoked potential (SSVEP)-based BCI. This study implemented and tested online signal processing methods in both time and frequency domains for detecting SSVEPs. The results of this study showed that the performance of the proposed cell-phone-based platform was comparable, in terms of the information transfer rate, with other BCI systems using bulky commercial EEG systems and personal computers. To the best of our knowledge, this study is the first to demonstrate a truly portable, cost-effective and miniature cell-phone-based platform for online BCIs.

Grozea C., Voinescu C.D., Fazli S.

In this paper, we present a new, low-cost dry electrode for EEG that is made of flexible metal-coated polymer bristles. We examine various standard EEG paradigms, such as capturing occipital alpha rhythms, testing for event-related potentials in an auditory oddball paradigm and performing a sensory motor rhythm-based event-related (de-) synchronization paradigm to validate the performance of the novel electrodes in terms of signal quality. Our findings suggest that the dry electrodes that we developed result in high-quality EEG recordings and are thus suitable for a wide range of EEG studies and BCI applications. Furthermore, due to the flexibility of the novel electrodes, greater comfort is achieved in some subjects, this being essential for long-term use.

Bedair S.S., Meyer C.D., Morgan B.

This paper outlines a low-cost multimaterial, integrated passives approach involving suspension wicking of high-K dielectric and ferromagnetic nanoparticles into capillaries comprising inductor and capacitor passive devices. The suspension is deposited into a “target well” and nanoparticles are delivered to the passive via fluidic self-assembly, resulting in inductor and capacitor value improvements. The universality of this approach has been demonstrated through the fabrication and testing of both MEMS inductors and capacitors on a single substrate, which would otherwise be fabrication-intense using traditional fabrication methods. This approach has demonstrated inductance improvements of 45% up to 500 MHz with roll-off in quality factor past 225 MHz after wicking of a NiFe2O4 nanoparticle core. In addition, capacitance was increased 400% and 600% after wicking of BaTiO3 nanoparticles/polymer composite into 1- and 2-mm-long capacitor constructs, respectively.

Liao L., Chen Y., Lin C., Chang J., Li M.

In this study, we report on using a 50-MHz functional photoacoustic microscopy (PAM) to transcranially image the cross-section and hemoglobin oxygenation (SO2) changes of single mouse cortical vessels in response to left forepaw electrical stimulation. Three difference levels of the cortical vessels (i.e., with different-sized diameters of 350, 100 and 55 μm) on activated regions were marked to measure their functional cross-section and SO2 changes as a function of time. Electrical stimulation of the mouse left forelimb was applied to evoke functional changes in vascular dynamics of the mouse somatosensory cortex. The applied current pulses were with a pulse frequency of 3 Hz, pulse duration of 0.2 ms, and pulse amplitude of 2 mA. The cerebrovascular cross-section changes, which indicate changes in cerebral blood volume (CBV), were probed by images acquired at 570 nm, a hemoglobin isosbestic point, while SO2 changes were monitored by the derivatives of 560-nm images normalized to 570-nm ones. The results show that vessel diameter and SO2 were significantly dilated and increased when compared with those of the controlled ones. In summary, the PAM shows its promise as a new imaging modality for transcranially functional quantification of single vessel diameter (i.e., CBV) and SO2 changes without any contrast agents applied during stimulation.

Karamian S.A., Carroll J.J.

The scattering of thermal neutrons from isomeric nuclei may include events in which the outgoing neutrons have increased kinetic energy. This process has been called inelastic neutron acceleration, or INNA, and occurs when the final nucleus, after emission of the neutron, is left in a state with lower energy than that of the isomer. The result, therefore, is an induced depletion of the isomer to the ground state. A cascade of several $\ensuremath{\gamma}$'s must accompany the neutron emission to release the high angular momentum of the initial isomeric state. INNA was previously observed in a few cases, and the measured cross sections were only in modest agreement with theoretical estimates. The most recent measurement of an INNA cross section was ${\ensuremath{\sigma}}_{\mathrm{INNA}}=258\ifmmode\pm\else\textpm\fi{}58 \mathrm{b}$ for neutron scattering by $^{177}\mathrm{Lu}$${}^{m}$. In the present work, an INNA cross section of ${\ensuremath{\sigma}}_{\mathrm{INNA}}=168 \ifmmode\pm\else\textpm\fi{} 33 \mathrm{b}$ was deduced from measurements of the total burnup of the high-spin, four-quasiparticle isomer $^{178}\mathrm{Hf}$${}^{m2}$ during irradiation by thermal neutrons. Statistical estimates for the probability of different reaction channels past neutron absorption were used in the analysis, and the deduced ${\ensuremath{\sigma}}_{\mathrm{INNA}}$ was compared to the theoretically predicted cross section.

Chua E., Wai-Chi Fang

This paper presents a highly integrated VLSI implementation of a mixed bio-signal lossless data compressor capable of handling multichannel electroencephalogram (EEG), electrocardiogram (ECG) and diffuse optical tomography (DOT) bio-signal data for reduced storage and communication bandwidth requirements in portable, wireless brain-heart monitoring systems used in hospital or home care settings. The compressor integrated in a multiprocessor brain-heart monitoring IC comprises 15 k gates and 12 kbits of RAM, occupying a total area of 58 k μm 2 in 65 nm CMOS technology. Results demonstrate an average compression ratio (CR) of 2.05, and a simulated power consumption of 170 μW at an operating condition of 24 MHz clock and 1.0 V core voltage. Nominal power savings of 43% and 47% at the transmitter can be achieved when employing Bluetooth and Zigbee transceivers, respectively.

von Cresce A., Xu K.

An electrolyte additive based on highly fluorinated phosphate ester structure was identified as being able to stabilize carbonate-based electrolytes on 5 V class cathode surfaces. The synthesis and structural analysis of the additive are described, and preliminary yet encouraging results from electrochemical characterization showed that this additive participates in forming a protective interphasial chemistry not only on transition metal oxide cathode at high voltage (∼5 V vs Li) but also on graphitic graphite at low voltage (∼0 V vs Li), making it possible to formulate an electrolyte supporting reversible Li + -intercalation chemistry in the coveted 5 V region.

Dash S., Dash D.K., Tripathy R.K., Pachori R.B.

Puiac A.V., Cioca L., Lakatos G.D., Groza A.

This study is the result of the need to research the visualization of brainwaves. The aim is based on the idea of using generative AI art systems as a method. Data visualization is an important part of understanding the evolution of the world around us. It offers the ability to see a representation that goes beyond numbers. Generative AI systems have gained the possibility of helping the process of visualizing data in new ways. This specific process includes real-time-generated artistic renderings of these data. This real-time rendering falls into the field of brainwave visualization, with the help of the EEG (electroencephalogram), which can serve here as input data for Generative AI systems. The brainwave measurement technology as a form of input to real-time generative AI systems represents a novel intersection of neuroscience and art in the field of neurofeedback art. The main question this paper hopes to address is as follows: How can brainwaves be effectively fed into generative AI art systems, and where can the outcome lead, in terms of progress? EEG data were successfully integrated with generative AI to create interactive art. The installation provided an immersive experience by moving the image with the change in the user’s mental focus, demonstrating the impact of EEG-based art.

Ma Y., Huang J., Liu C., Shi M.

Brain-computer interfaces (BCIs) have garnered significant research attention, yet their complexity has hindered widespread adoption in daily life. Most current electroencephalography (EEG) systems rely on wet electrodes and numerous electrodes to enhance signal quality, making them impractical for everyday use. Portable and wearable devices offer a promising solution, but the limited number of electrodes in specific regions can lead to missing channels and reduced BCI performance. To overcome these challenges and enable better integration of BCI systems with external devices, this study developed an EEG signal acquisition platform (Gaitech BCI) based on the Robot Operating System (ROS) using a 10-channel dry electrode EEG device. Additionally, a multi-scale channel attention selection network based on the Squeeze-and-Excitation (SE) module (SEMSCS) is proposed to improve the classification performance of portable BCI devices with limited channels. Steady-state visual evoked potential (SSVEP) data were collected using the developed BCI system to evaluate both the system and network performance. Offline data from ten subjects were analyzed using within-subject and cross-subject experiments, along with ablation studies. The results demonstrated that the SEMSCS model achieved better classification performance than the comparative reference model, even with a limited number of channels. Additionally, the implementation of online experiments offers a rational solution for controlling external devices via BCI.

Wang H., Qi Y., Yao L., Wang Y., Farina D., Pan G.

Aldhaheri T.A., Kulkarni S.B., Bhise P.R., Ghaleb B.

Chang H., Sun Y., Lu S., Lin D.

Injection molding is a common plastic processing technique that allows melted plastic to be injected into a mold through pressure to form differently shaped plastic parts. In injection molding, in-mold electronics (IME) can include various circuit components, such as sensors, amplifiers, and filters. These components can be injected into the mold to form a whole within the melted plastic and can therefore be very easily integrated into the molded part. The brain–computer interface (BCI) is a direct connection pathway between a human or animal brain and an external device. Through BCIs, individuals can use their own brain signals to control these components, enabling more natural and intuitive interactions. In addition, brain–computer interfaces can also be used to assist in medical treatments, such as controlling prosthetic limbs or helping paralyzed patients regain mobility. Brain–computer interfaces can be realized in two ways: invasively and noninvasively, and in this paper, we adopt a noninvasive approach. First, a helmet model is designed according to head shape, and second, a printed circuit film is made to receive EEG signals and an IME injection mold for the helmet plastic parts. In the electronic film, conductive ink is printed to connect each component. However, improper parameterization during the injection molding process can lead to node displacements and residual stress changes in the molded part, which can damage the circuits in the electronic film and affect its performance. Therefore, in this paper, the use of the BCI molding process to ensure that the node displacement reaches the optimal value is studied. Second, the multistrategy differential evolutionary algorithm is used to optimize the injection molding parameters in the process of brain–computer interface formation. The relationship between the injection molding parameters and the actual target value is investigated through Latin hypercubic sampling, and the optimized parameters are compared with the target parameters to obtain the optimal parameter combination. Under the optimal parameters, the node displacement can be optimized from 0.585 to 0.027 mm, and the optimization rate can reach 95.38%. Ultimately, by detecting whether the voltage difference between the output inputs is within the permissible range, the reliability of the brain–computer interface after node displacement optimization can be evaluated.

Ferreira L.G., Pimenta T.C.

Gramann K.

Gao X., Yin L., Tian S., Huang Y., Ji Q.

Huang X., Chen S., Wei C.

Gramann K.

AbstractBased on increasing incidents of mental ill-health associated with living in dense urban environments, the field of Neurourbanism developed rapidly, aiming at identifying and improving urban factors that impact the health of city dwellers. Neurourbanism and the closely related field of Neuro-Architecture have seen a surge in studies using mobile electroencephalography (EEG) to investigate the impact of the built and natural environment on human brain activity moving from the laboratory into the real world. This trend predominantly arises from the ready availability of affordable and portable consumer hardware, which not only guarantees operational simplicity but also frequently incorporates automated data analysis functions. This significantly streamlines the process of EEG data acquisition, analysis, and interpretation, seemingly challenging the necessity of specialized expertise in the method of EEG or neurosciences in general. As a consequence, numerous studies in the field of Neurourbanism have used such off-the-shelf systems in laboratory and real-world experimental protocols including active movement of participants through the environment. However, the recording and analysis of EEG data entails numerous requisites, the disregard of which may culminate in errors during data acquisition, processing, and subsequent interpretation, potentially compromising the scientific validity of the outcomes. The often relatively low number of electrodes offered by affordable and portable consumer EEG systems further restricts specific analyses approaches to the low-dimensional EEG data. Crucially, a large part of Neurourbanism studies used black-box analyses provided by such consumer systems or incorrectly applied complex data-driven analyses methods that are incompatible with the recorded low-dimensional data. The current manuscript delineates the prerequisites concerning EEG hardware and analytical methodologies applicable to stationary and mobile EEG protocols, whether conducted within a controlled laboratory environment or in real-world settings. It conducts a comprehensive review of EEG studies within the domain of Neurourbanism and Neuro-Architecture, assessing their adherence to these prerequisites. The findings reveal severe deficiencies in the utilization of hardware and data processing methods, thereby rendering these studies unsuitable for scientific scrutiny. Consequently, the present paper provides guidelines for the selection of EEG hardware and analytical strategies for researchers engaged in mobile EEG recordings, be it within a laboratory or real-world context, aimed at steering future investigations in the field of Neurourbanism and Neuro-Architecture.

Taskasaplidis G., Fotiadis D.A., Bamidis P.D.

Dong C., Lee C., Lee D., Moon S., Park W.

Microneedle array electrode (MNAE) have demonstrated high reliability in signal recording and electrode-skin interface impedance, making them a promising option for bio-signal sensing. In this study, we fabricated barbed microneedles for MNAE using standard photolithography and PDMS thermal expansion techniques. By comparing experiments with Ag/AgCl electrodes, we observed the following findings: (1) The force required to pull out barbed MNs from a soft parafilm membrane was significantly higher than that for barbless MNs. (2) We successfully used these MNAE prototypes to record electrocardiograms (ECGs) both in static and dynamic states, resulting in higher amplitudes. Additionally, we measured EII and evaluated the performance of the proposed dry microneedle electrodes compared to traditional wet electrodes with Ag/AgCl electrode.

Hong W.

Abstract Mobile health (mHealth) with continuous real-time monitoring is leading the era of digital medical convergence. Wearable devices and smartphones optimized as personalized health management platforms enable disease prediction, prevention, diagnosis, and even treatment. Ubiquitous and accessible medical services offered through mHealth strengthen universal health coverage to facilitate service use without discrimination. This viewpoint investigates the latest trends in mHealth technology, which are comprehensive in terms of form factors and detection targets according to body attachment location and type. Insights and breakthroughs from the perspective of mHealth sensing through a new form factor and sensor-integrated display overcome the problems of existing mHealth by proposing a solution of smartphonization of wearable devices and the wearable deviceization of smartphones. This approach maximizes the infinite potential of stagnant mHealth technology and will present a new milestone leading to the popularization of mHealth. In the postpandemic era, innovative mHealth solutions through the smartphonization of wearable devices and the wearable deviceization of smartphones could become the standard for a new paradigm in the field of digital medicine.

Lee H., Lee C.