Search Results (60)

We present a large-scale neuromodulation system-on-chip (SoC) that integrates a 128-channel neural recording and 32-channel stimulation ASIC designed for a wide range of neuroscientific applications. Each recording channel achieves low-noise performance (~4 μV_rms) with a configurable bandwidth of 0.05 Hz–7.5 kHz and supports 16-bit digitization with scalable sampling rates up to 30 kS/s. To enhance signal quality, the ASIC includes an adjustable digital high-pass filter and a fast-settling function for rapid recovery from stimulation artifacts. SoC also incorporates on-chip electrode-impedance measurements as a built-in safety feature by reusing the recording channels. The stimulation subsystem generates current-controlled monopolar biphasic pulses with a high compliance voltage of ±6 V using standard low-voltage (1.8 V/3.3 V) CMOS devices. Each of the 32 stimulation channels provides arbitrary 9-bit programmable waveforms and dual current modes (4 μA/bit and 8 μA/bit), supporting both fine-resolution microstimulation and high-current applications such as spinal-cord and deep-brain stimulation. On-chip charge-balancing switches in each channel further ensure safe and reliable stimulation delivery. The SoC supports digital communication via a standard SPI with both 3.3 V CMOS and low-voltage differential signaling options and integrates all required analog references and low-dropout regulators. The prototype was fabricated in a standard 180 nm CMOS process, occupying 31.92 mm² (equivalently, 0.2 mm² per recording-and-stimulation channel), and was fully validated through benchtop measurements and in vitro experiments. Full article

Brain–Computer Interfaces (BCIs) offer a non-invasive pathway for restoring motor function, particularly for individuals with limb loss. This review explored the effectiveness of Electroencephalography (EEG) and function Near-Infrared Spectroscopy (fNIRS) in decoding Motor Imagery (MI) movements for both offline and online BCI systems. EEG has been the dominant non-invasive neuroimaging modality due to its high temporal resolution and accessibility; however, it is limited by high susceptibility to electrical noise and motion artifacts, particularly in real-world settings. fNIRS offers improved robustness to electrical and motion noise, making it increasingly viable in prosthetic control tasks; however, it has an inherent physiological delay. The review categorizes experimental approaches based on modality, paradigm, and study type, highlighting the methods used for signal acquisition, feature extraction, and classification. Results show that while offline studies achieve higher classification accuracy due to fewer time constraints and richer data processing, recent advancements in machine learning—particularly deep learning—have improved the feasibility of online MI decoding. Hybrid EEG–fNIRS systems further enhance performance by combining the temporal precision of EEG with the spatial specificity of fNIRS. Overall, the review finds that predicting online imagined movement is feasible, though still less reliable than motor execution, and continued improvements in neuroimaging integration and classification methods are essential for real-world BCI applications. Broader dissemination of recent advancements in MI-based BCI research is expected to stimulate further interdisciplinary collaboration among roboticists, neuroscientists, and clinicians, accelerating progress toward practical and transformative neuroprosthetic technologies. Full article

Medical physicists are transforming physiological measurements and electrophysiological applications by addressing challenges like motion artifacts and regulatory compliance through advanced signal processing, artificial intelligence (AI), and statistical rigor. Their innovations in wearable electrophysiology achieve 8–12 dB signal-to-noise ratio (SNR) improvements in EEG, 60% motion artifact reduction, and 94.2% accurate AI-driven arrhythmia detection at 12 μW power. In precision neurology, machine learning (ML) with evoked potentials (EPs) predicts spinal cord injury (SCI) recovery and multiple sclerosis (MS) progression with 79.2% accuracy based on retrospective data from 560 SCI/MS patients. By integrating multimodal data (EPs, MRI), developing quantum sensors, and employing federated learning, these can enhance diagnostic precision and prognostic accuracy. Clinical applications span epilepsy, stroke, cardiac monitoring, and chronic pain management, reducing diagnostic errors by 28% and optimizing treatments like deep brain stimulation (DBS). In this paper, we review the current state of wearable devices and provide some insight into possible future directions. Embedding medical physicists into standardization efforts is critical to overcoming barriers like quantum sensor power consumption, advancing personalized, evidence-based healthcare. Full article

Neurodegenerative disorders such as Alzheimer’s disease (AD), Parkinson’s disease (PD), and multiple sclerosis (MS) are marked by progressive network dysfunction that challenges conventional, protocol-based neurorehabilitation. In parallel, neuromodulation, encompassing deep brain stimulation (DBS), transcranial magnetic stimulation (TMS), transcranial direct current stimulation (tDCS), vagus nerve stimulation (VNS), and artificial intelligence (AI), has matured rapidly, offering complementary levers to tailor therapy in real time. This narrative review synthesizes current evidence at the intersection of AI and neuromodulation in neurorehabilitation, focusing on how data-driven models can personalize stimulation and improve functional outcomes. We conducted a targeted literature synthesis of peer-reviewed studies identified via PubMed, Embase, Scopus, and reference chaining, prioritizing recent clinical and translational reports on adaptive/closed-loop systems, predictive modeling, and biomarker-guided protocols. Across indications, convergent findings show that AI can optimize device programming, enable state-dependent stimulation, and support clinician decision-making through multimodal biomarkers derived from neural, kinematic, and behavioral signals. Key barriers include data quality and interoperability, model interpretability and safety, and ethical and regulatory oversight. Here we argue that AI-enhanced neuromodulation reframes neurorehabilitation from static dosing to adaptive, patient-specific care. Advancing this paradigm will require rigorous external validation, standardized reporting of control policies and artifacts, clinician-in-the-loop governance, and privacy-preserving analytics. Full article

Transcranial magnetic stimulation combined with electroencephalography (TMS-EEG) has emerged as a transformative tool for probing cortical dynamics with millisecond precision. This review examines the state-dependent nature of TMS-EEG, a critical yet underexplored dimension influencing measurement reliability and clinical applicability. By integrating TMS’s neuromodulatory capacity with EEG’s temporal resolution, this synergy enables real-time analysis of brain network dynamics under varying neural states. We delineate foundational mechanisms of TMS-evoked potentials (TEPs), discuss challenges posed by temporal and inter-individual variability, and evaluate advanced paradigms such as closed-loop and task-embedded TMS-EEG. The former leverages real-time EEG feedback to synchronize stimulation with oscillatory phases, while the latter aligns TMS pulses with task-specific cognitive phases to map transient network activations. Current limitations—including hardware constraints, signal artifacts, and inconsistent preprocessing pipelines—are critically analyzed. Future directions emphasize adaptive algorithms for neural state prediction, phase-specific stimulation protocols, and standardized methodologies to enhance reproducibility. By bridging mechanistic insights with personalized neuromodulation strategies, state-dependent TMS-EEG holds promise for advancing both basic neuroscience and precision medicine, particularly in psychiatric and neurological disorders characterized by dynamic neural dysregulation. Full article

Improving the aqueous solubility of poorly soluble pharmaceuticals is essential for accurate pharmacotoxicological testing, but the biological safety of solubilizers and hydrotropic agents used for this purpose requires careful evaluation. This study assessed the acute toxicity, physiological parameters (heart rate, claw and appendage movement), behavioral responses (swimming speed), and embryotoxicity of 15 commonly used solubilizers and hydrotropes using Daphnia magna as a biological model. Compounds included surfactants (polysorbate 20 (Tween 20), polysorbate 80 (Tween 80), sodium lauryl sulfate (SLS)), sulfonated hydrotropes (sodium xylene sulfonate (SXS), sodium benzenesulfonate (SBS), sodium p-toluenesulfonate (PTS), sodium 1,3-benzenedisulfonate (SBDS)), and solubilizing solvents (dimethyl sulfoxide (DMSO), glycerol (GLY), propylene glycol (PDO), dimethylformamide (DMF), N,N’-Dimethylbenzamide (DMBA), N,N-Diethylnicotinamide (DENA), N,N-Dimethylurea (DMU), urea). Acute lethality was evaluated across concentration ranges appropriate to each compound group (e.g., 0.0005–0.125% for surfactants; up to 5% for less toxic solvents). Surfactants exhibited extreme toxicity, with Tween 20 and SLS causing 100% lethality even at 0.0005%, while Tween 80 induced 40–50% lethality at that concentration. In contrast, DMSO, GLY, and PDO showed low acute toxicity, maintaining normal heart rate (202–395 bpm), claw and appendage movement, and swimming speed at ≤1%, though embryotoxicity became evident at higher concentrations (≥1–2%). SXS, SBS, PTS, and SBDS displayed clear dose-dependent toxicity but were generally tolerated up to 0.05%. DMBA, DENA, and DMU caused physiological suppression, including reduced heart rate (e.g., DMBA: 246 bpm vs. control 315 bpm) and impaired mobility. Behavioral assays revealed biphasic effects for DMSO and DMBA, with early stimulation (24 h) followed by inhibition (48 h). Embryotoxicity assays demonstrated significant morphological abnormalities and developmental delays at elevated concentrations, especially for DMSO, GLY, and PDO. Overall, DMSO, GLY, PDO, SXS, and DMF can be safely used at tightly controlled concentrations in Daphnia magna toxicity assays to ensure accurate screening without solvent-induced artifacts. Full article

Research interest in the application of electroencephalogram (EEG) as a non-invasive diagnostic tool for the automated detection of neurodegenerative diseases is growing. Open-access datasets have become crucial for researchers developing such methodologies. Our previously published open-access dataset of resting-state (eyes-closed) EEG recordings from patients with Alzheimer’s disease (AD), frontotemporal dementia (FTD), and cognitively normal (CN) controls has attracted significant attention. In this paper, we present a complementary dataset consisting of eyes-open photic stimulation recordings from the same cohort. The dataset includes recordings from 88 participants (36 AD, 23 FTD, and 29 CN) and is provided in Brain Imaging Data Structure (BIDS) format, promoting consistency and ease of use across research groups. Additionally, a fully preprocessed version is included, using EEGLAB-based pipelines that involve filtering, artifact removal, and Independent Component Analysis, preparing the data for machine learning applications. This new dataset enables the study of brain responses to visual stimulation across different cognitive states and supports the development and validation of automated classification algorithms for dementia detection. It offers a valuable benchmark for both methodological comparisons and biological investigations, and it is expected to significantly contribute to the fields of neurodegenerative disease research, biomarker discovery, and EEG-based diagnostics. Full article

Stimulated Raman Scattering (SRS) nanoscopy imaging offers unprecedented insights into tissue molecular architecture but often requires stitching multiple high-resolution tiles to capture large fields of view. This process is time-consuming and frequently introduces vignetting artifacts—grid-like intensity fluctuations that degrade image quality and hinder downstream quantitative analyses and processing such as super-resolution deconvolution. We present VIgnetted Stitched-Tile Adjustment using Morphological Adaptive Processing (VISTAmap), a simple tool that corrects these shading artifacts directly on the final stitched image. VISTAmap automatically detects the tile grid configuration by analyzing intensity frequency variations and then applies sequential morphological operations to homogenize the image. In contrast to conventional approaches that require increased tile overlap or pre-acquisition background sampling, VISTAmap offers a pragmatic, post-processing solution without the need for separate individual tile images. This work addresses pressing concerns by delivering a robust, efficient strategy for enhancing mosaic image uniformity in modern nanoscopy, where the smallest details make tremendous impacts. Full article

Whilst balance disturbances are common in people with advanced Parkinson’s disease, it has not previously been possible to record vestibular evoked myogenic potentials (VEMPs), and thus otolithic function, during monopolar deep brain stimulation (DBS) due to an overwhelming number of signal artifacts. A µVEMP device has been developed with parameters to allow VEMP recording during monopolar DBS. The aim of this proof-of-concept study was to ascertain whether, during DBS, VEMP responses could be accurately identified after signal filtering recordings from the µVEMP device. Both cervical and ocular VEMP responses to taps and clicks were recorded with the µVEMP device in five Parkinson’s disease patients with monopolar deep brain stimulation. Additionally, VEMP responses were recorded in one patient whose deep brain stimulation was switched ON and OFF to allow a direct comparison of the signals. Customised post-filtering analysis allowed successful VEMP response extraction from signal noise in all five patients with deep brain stimulation ON. VEMP responses with deep brain stimulation ON after filtering were similar to VEMP responses with deep brain stimulation OFF, validating the filtering analysis. We present the first study to record VEMP signals with monopolar deep brain stimulation using a µVEMP device coupled with customised post-filtering. This finding will allow patients to be assessed without requiring adjustment of their therapeutic deep brain stimulation. Full article

Background/Objectives: Preliminary observations support the view that spinal cord epidural stimulation (scES) combined with trunk-specific training can improve trunk stability during functional activities in individuals with thoracic spinal cord injury (SCI). We studied the acute effects of trunk-specific stimulation on sitting postural control. Methods: Twenty-three individuals with severe cervical SCI were implanted with an epidural stimulator. Postural control was assessed before any activity-based training, without and with trunk-specific scES. In particular, participants performed sitting with upright posture, forward/back lean, and lateral lean activities while sitting on a standard therapy mat. Full-body kinematics and trunk electromyography (EMG) were acquired. Anterior-posterior and lateral trunk displacement along with trunk velocity in all four directions were obtained and used to classify postural control responses. Results: Compared to no stimulation, application of trunk-specific scES led to trunk anterior–posterior displacement increases during forward/back lean (2.79 ± 0.97 cm; p-value = 0.01), and trunk lateral displacement increases during lateral lean (2.19 ± 0.79 cm; p-value = 0.01). After digital filtering of stimulation artifacts, EMG root mean square amplitudes for bilateral external oblique, rectus abdominus, and erector spinae muscles were higher with stimulation for all activities (all p-values < 0.03). Conclusions: The results indicate improvements in trunk lean distances and muscle activation when leaning activities are performed with trunk-specific epidural stimulation. Full article

Background/Objectives: Before a cochlear implant is considered, patients undergo various audiological tests to assess their suitability. One key test measures the auditory brainstem response (ABR) to acoustic stimuli. However, in some cases, even with maximum sound stimulation, no response is detected. Methods: The promontory test involves electrical stimulation near the auditory nerve, allowing patients to associate the sensation. Ideally, the electrode is placed in the middle ear after opening the eardrum. This method, along with trans-tympanic electrically evoked ABR in local anesthesia (LA-TT-EABR) and the cortical equivalent (LA-TT-EALR), helps assess the auditory nerve’s existence and excitability. The TympEALR test, utilizing a “tympanic LA-TT-EALR”, provides an alternative measurement. Previous research has shown the possibility of deriving brainstem and cortical potentials through trans-tympanic electrical stimulation, allowing for objective assessment of the auditory nerve’s integrity and potentially objectifying patient sensations. Results: Sixteen patients have been tested using TympEALR. In seven of these, we found a positive response. The morphology was similar to other electrically evoked cortical auditory responses (EALR), e.g., using cochlear implants or trans-tympanic stimulation electrodes. We observed a higher influence of electrical artifacts than in other EALRs. Conclusions: TympEALR showed positive results in nearly half of the study participants, potentially avoiding invasive procedures. TympEALR can be a valuable alternative to trans-tympanic methods. More research is needed to determine if a negative result suggests against cochlear implantation. Full article

Traumatic peripheral nerve injuries (PNI), present with symptoms ranging from pain to loss of motor and sensory function. Difficulties in intraoperative visual assessment of nerve functional status necessitate intraoperative nerve conduction studies (INCSs) by neurosurgeons and neurologists to determine the presence of functioning axons in the zone of a PNI. This process, also referred to as nerve “inching”, uses a set of stimulating and recording electrode hooks to lift the injured nerve from the surrounding surgical field and to determine whether an electrical stimulus can travel through the zone of injury. However, confounding electrical signal artifacts can arise from the current workflow and electrode design, particularly from the mandatory lifting of the nerve, complicating the definitive assessment of nerve function and neurosurgical treatment decision-making. The objective of this study is to describe the design process and verification testing of our group’s newly designed stimulating and recording electrodes that do not require the lifting or displacement of the injured nerve during INCSs. Ergonomic in vivo analysis of the device within a porcine model demonstrated successful intraoperative manipulation of the device, while quantitative nerve action potential (NAP) signal analysis with an ex vivo simulated “inching” procedure on healthy non-human primate nerve tissue demonstrated excellent reproducible recorded NAP fidelity and the absence of NAP signal artifacts at all points of recording. Lastly, electrode pullout force testing determined maximum forces of 0.43 N, 1.57 N, and 3.61 N required to remove the device from 2 mm, 5 mm, and 1 cm nerve models, respectively, which are well within established thresholds for nerve safety. These results suggest that these new electrodes can safely and successfully perform accurate PNI assessment without the presence of artifacts, with the potential to improve the INCS standard of care while remaining compatible with currently used neurosurgical technology, infrastructure, and clinical workflows. Full article

When pharmaceutical treatments for Parkinson’s Disease (PD) are no longer effective, Deep Brain Stimulation (DBS) surgery, a procedure that entails the stimulation of the Subthalamic Nucleus (STN), is another treatment option. However, the success rate of this surgery heavily relies on the precise location of the STN, as well as the correct positioning of the stimulation electrode. In order to ensure the correct location, Micro-Electrode Recordings (MERs) are analyzed. During surgery, MERs capture brain signals while inserted in the brain, receiving different brain activity depending on the crossed brain area. The location of the STN is guaranteed when brain signals from MERs meet certain criteria. Nevertheless, MER signals are sensitive to various artifacts coming from machinery or other electrical equipment in the operating theater; patient activity; and electrode motion. These all lower the signal-to-noise ratio of the MER signals. MER signals are stochastic, multicomponent, transient, and non-stationary in nature, and they contain multi-unit neural activity in the form of spikes and artefacts. Thus, accurately defining that MERs are located in the STN is not an easy task. This work analyzes relevant features from MER, based on analyzing spike activity and local field signals. Six different classification algorithms are used, together with the optimal input feature selection. The algorithms are trained using supervised Leave-One-Out Cross-Validation. MER data were collected in a real scenario from 14 PD patients during DBS implantation surgery. The dataset is publicly available. The results derived from the use of this method show an accuracy of up to 100% in detecting where the MER electrode is located in the STN brain area. Full article

Tenosynovial giant cell tumor (TSGCT) is a rare soft tissue tumor that involves the synovial lining of joints, bursae, and tendon sheaths, primarily affecting young patients (usually in the fourth decade of life). The tumor comprises two subtypes: the localized type (L-TSGCT) and the diffuse type (D-TSGCT). Although these subtypes share histological and genetic similarities, they present a different prognosis. D-TSGCT tends to exhibit local aggressiveness and a higher recurrence rate compared to L-TSGCT. Magnetic resonance imaging (MRI) is the preferred diagnostic tool for both the initial diagnosis and for treatment planning. When interpreting the initial MRI of a suspected TSGCT, it is essential to consider: (i) the characteristic findings of TSGCT—evident as low to intermediate signal intensity on both T1- and T2-weighted images, with a blooming artifact on gradient-echo sequences due to hemosiderin deposition; (ii) the possibility of D-TSGCT—extensive involvement of the synovial membrane with infiltrative margin; and (iii) the resectability and extent—if resectable, synovectomy is performed; if not, a novel systemic therapy involving colony-stimulating factor 1 receptor inhibitors is administered. In the interpretation of follow-up MRIs of D-TSGCTs after treatment, it is crucial to consider both tumor recurrence and potential complications such as osteoarthritis after surgery as well as the treatment response after systemic treatment. Given its prevalence in young adult patents and significant impact on patients’ quality of life, clinical trials exploring new agents targeting D-TSGCT are currently underway. Consequently, understanding the characteristic MRI findings of D-TSGCT before and after treatment is imperative. Full article

Binaural beats are a low-frequency form of acoustic stimulation that may be heard between 200 and 900 Hz and can help reduce anxiety as well as alter other psychological situations and states by affecting mood and cognitive function. However, prior research has only looked at the impact of binaural beats on state and trait anxiety using the STA-I scale; the level of anxiety has not yet been evaluated, and for the removal of artifacts the improper selection of wavelet parameters reduced the original signal energy. Hence, in this research, the level of anxiety when hearing binaural beats has been analyzed using a novel optimized wavelet transform in which optimized wavelet parameters are extracted from the EEG signal using the flower pollination algorithm, whereby artifacts are removed effectively from the EEG signal. Thus, EEG signals have five types of brainwaves in the existing models, which have not been analyzed optimally for brainwaves other than delta waves nor has the level of anxiety yet been analyzed using binaural beats. To overcome this, deep convolutional neural network (CNN)-based signal processing has been proposed. In this, deep features are extracted from optimized EEG signal parameters, which are precisely selected and adjusted to their most efficient values using the flower pollination algorithm, ensuring minimal signal energy reduction and artifact removal to maintain the integrity of the original EEG signal during analysis. These features provide the accurate classification of various levels of anxiety, which provides more accurate results for the effects of binaural beats on anxiety from brainwaves. Finally, the proposed model is implemented in the Python platform, and the obtained results demonstrate its efficacy. The proposed optimized wavelet transform using deep CNN-based signal processing outperforms existing techniques such as KNN, SVM, LDA, and Narrow-ANN, with a high accuracy of 0.99%, precision of 0.99%, recall of 0.99%, F1-score of 0.99%, specificity of 0.999%, and error rate of 0.01%. Thus, the optimized wavelet transform with a deep CNN can perform an effective decomposition of EEG data and extract deep features related to anxiety to analyze the effect of binaural beats on anxiety levels. Full article