Due to large intra-subject and inter-subject variabilities of electroencephalogram (EEG) signals, EEG-based brain-computer interfaces (BCIs) usually need subject-specific calibration to tailor the decoding algorithm for each new subject, which is time-consuming and user-unfriendly, hindering their real-world applications. Transfer learning (TL) has been extensively used to expedite the calibration, by making use of EEG data from other subjects/sessions. An important consideration in TL for EEG-based BCIs is to reduce the data distribution discrepancies among different subjects/sessions, to avoid negative transfer. Euclidean alignment (EA) was proposed in 2020 to address this challenge. Numerous experiments from 13 different BCI paradigms demonstrated its effectiveness and efficiency. This paper revisits EA, explaining its procedure and correct usage, introducing its applications and extensions, and pointing out potential new research directions. It should be very helpful to BCI researchers, especially those who are working on EEG signal decoding.

Objective.Cerebellar transcranial magnetic stimulation (TMS) has been proposed to suppress limb tremors in essential tremor (ET), but mixed results have been reported so far, both when pulses are applied repetitively TMS (rTMS) and in bursts. We aim to investigate the cellular effects of TMS on the cerebellum under ET through numerical simulations.Approach.A computational model of the olivo-cerebello-thalamocortical pathways exhibiting the main neural biomarkers of ET (i.e. circuit-wide tremor-locked neural oscillations) was expanded to incorporate the effects of TMS-induced electric field (E-field) on Purkinje cells. TMS pulse amplitude, frequency, and temporal pattern were varied, and the resultant effects on ET biomarkers were assessed. Four levels of cellular response to TMS were considered, ranging from low to high cell recruitment underneath the coil, and three stimulation patterns were tested, i.e. rTMS, irregular TMS (ir-TMS, pulses were arranged according to Sobol sequences with average frequency matching rTMS), and phase-locked TMS (PL-TMS).Main results.rTMS can suppress ET oscillations, but its efficacy depends on tremor frequency and recruitment level, with these factors shaping a narrow range of effective settings. The ratio between tremor and rTMS frequencies also affects the neural response and further narrows the span of viable settings, while ir-TMS is ineffective. PL-TMS is highly effective and robust against changes to cell recruitment level and tremor frequency. Across all scenarios, PL-TMS provides a rapid (i.e. within seconds) suppression of tremor oscillations and, when both PL-TMS and rTMS are effective, the time to tremor suppression decreases by 50% or more in PL-TMS versus rTMS. At the cellular level, PL-TMS operates by disrupting the synchronization along the olivo-cerebellar loop, and the preferred phases map onto the mid-region of the silent period between complex spikes of the Purkinje cells.Significance.Cerebellar PL-TMS can provide robust suppression of ET oscillations while operating within safety boundaries.

Objective.Recently, a novel language training using an auditory brain-computer interface (BCI) based on electroencephalogram recordings has been proposed for chronic stroke patients with aphasia. Tested with native German patients, it has shown significant and medium to large effect sizes in improving multiple aspects of language. During the training, the auditory BCI system delivers word stimuli using six spatially arranged loudspeakers. As delivering the word stimuli via headphones reduces spatial cues and makes the attention to target words more difficult, we investigate the influence of added pitch information. While pitch modulations have shown benefits for tone stimuli, they have not yet been investigated in the context of language stimuli.Approach.The study translated the German experimental setup into Dutch. Seventeen native Dutch speakers participated in a single session of an exploratory study. An incomplete Dutch sentence cued them to listen to a target word embedded into a sequence of comparable non-target words while an electroencephalogram was recorded. Four conditions were compared within-subject to investigate the influence of pitch modulation: presenting the words spatially from six loudspeakers without (6D) and with pitch modulation (6D-Pitch), via stereo headphones with simulated spatial cues and pitch modulation (Stereo-Pitch), and via headphones without spatial cues or pitch modulation (Mono).Main results.Comparing the 6D conditions of both language setups, the Dutch setup could be validated. For the Dutch setup, the binary AUC classification score in the 6D and the 6D-Pitch condition were 0.75 and 0.76, respectively, and adding pitch information did not significantly alter the binary classification accuracy of the event-related potential responses. The classification scores in the 6D condition and the Stereo-Pitch condition were on the same level.Significance.The competitive performance of pitch-modulated word stimuli suggests that the complex hardware setup of the 6D condition could be replaced by a headphone condition. If future studies with aphasia patients confirm the effectiveness and higher usability of a headphone-based language rehabilitation training, a simplified setup could be implemented more easily outside of clinics to deliver frequent training sessions to patients in need.

Objective. Our objective was to perform a complete analysis ofin-vitroimpedance data for sputtered iridium oxide film (SIROF) micro-electrodes. The analysis included quantification of the stochastic and bias error structure and development of a process model that accounted for the chemistry and physics of the electrode-electrolyte interface.Approach. The measurement model program was used to analyze electrochemical impedance spectroscopy (EIS) data for SIROF micro-electrodes at potentials ranging from -0.4 to +0.6 V(Ag|AgCl). The frequency range used for the analysis was that determined to be consistent with the Kramers-Kronig relations. Interpretation of the data was enabled by truncating frequencies at which the ohmic impedance influenced the impedance.Main results. An interpretation model was developed that considered the impedance of the bare surface and the contribution of a porous component, based on the de Levie model of porous electrodes. The influence of iridium oxidation state on impedance was included. The proposed model fit all 36 EIS spectra well. The effective capacitance of the SIROF system ranged from 32 mF cm-2at -0.4 V(Ag|AgCl) to a maximum of 93 mF cm-2at 0.2 and 0.4 V(Ag|AgCl).Significance. The model developed to interpret the impedance response of neural stimulation electrodesin vitroguides model development forin-vivostudies.

Objective.Cochlear implants are among the few clinical interventions for people with severe or profound hearing loss. However, current spread during monopolar electrical stimulation results in poor spectral resolution, prompting the exploration of optical stimulation as an alternative approach. Enabled by introducing light-sensitive ion channels into auditory neurons (optogenetics), optical stimulation has been shown to activate a more discrete neural area with minimal overlap between each frequency channel during simultaneous stimulation. However, the utility of optogenetic approaches is uncertain due to the low fidelity of responses to light and high-power requirements compared to electrical stimulation.Approach.Hybrid stimulation, combining sub-threshold electrical and optical pulses, has been shown to improve fidelity and use less light, but the impact on spread of activation and channel summation using a translatable, multi-channel hybrid implant is unknown. This study examined these factors during single channel and simultaneous multi-channel hybrid stimulation in transgenic mice expressing the ChR2/H134R opsin. Acutely deafened mice were implanted with a hybrid cochlear array containing alternating light emitting diodes and platinum electrode rings. Spiking activity in the inferior colliculus was recorded during electrical-only or hybrid stimulation in which optical and electrical stimuli were both at sub-threshold intensities. Thresholds, spread of activation, and threshold shifts during simultaneous hybrid stimulation were compared to electrical-only stimulation.Main results.The electrical current required to reach activation threshold during hybrid stimulation was reduced by 7.3 dB compared to electrical-only stimulation (p< 0.001). The activation width measured at two levels of discrimination above threshold and channel summation during simultaneous hybrid stimulation were significantly lower compared to electrical-only stimulation (p< 0.05), but there was no spatial advantage of hybrid stimulation at higher electrical stimulation levels.Significance.Reduced channel interaction would facilitate multi-channel simultaneous stimulation, thereby enhancing the perception of temporal fine structure which is crucial for music and speech in noise.

Objective.Major depressive disorder (MDD) is a widespread mental disorder that affects health. Many methods combining electroencephalography (EEG) with machine learning or deep learning have been proposed to objectively distinguish between MDD and healthy individuals. However, most current methods detect depression based on multichannel EEG signals, which constrains its application in daily life. The context in which EEG is obtained can vary in terms of study designs and EEG equipment settings, and the available depression EEG data is limited, which could also potentially lessen the efficacy of the model in differentiating between MDD and healthy subjects. To solve the above challenges, a depression detection model leveraging transfer learning with the single-channel EEG is advanced.Approach.We utilized a pretrained ResNet152V2 network to which a flattening layer and dense layer were appended. The method of feature extraction was applied, meaning that all layers within ResNet152V2 were frozen and only the parameters of the newly added layers were adjustable during training. Given the superiority of deep neural networks in image processing, the temporal sequences of EEG signals are first converted into images, transforming the problem of EEG signal categorization into an image classification task. Subsequently, a cross-subject experimental strategy was adopted for model training and performance evaluation.Main results.The model was capable of precisely (approaching 100% accuracy) identifying depression in other individuals by employing single-channel EEG samples obtained from a limited number of subjects. Furthermore, the model exhibited superior performance across four publicly available depression EEG datasets, thereby demonstrating good adaptability in response to variations in EEG caused by the context.Significance.This research not only highlights the impressive potential of deep transfer learning techniques in EEG signal analysis but also paves the way for innovative technical approaches to facilitate early diagnosis of associated mental disorders in the future.

Objective.With the recent development of visual evoked potential (VEP) based brain-computer interfaces (BCIs), the stimulus paradigm has been continuously innovated, in which the pursuit of higher BCI performance and better user experience has become indispensable.Approach.To optimize the stimulus paradigm, a 12-target online BCI system was designed in this study by adopting flicker for steady-state VEPs, Newton's ring for steady-state motion VEP, and frame rate based video stimulus, respectively. The signal characteristics of VEP, classification accuracy, and user experience of the three stimulus paradigms were quantitatively evaluated and compared.Main results.The online information transfer rates for the three stimulus paradigms were 53.77 bits min-1, 51.41 ± 3.55 bits min-1, and 52.07 ± 3.09 bits min-1, respectively. The video stimulus had a significantly better user experience, while the flicker stimulus showed the worst.Significance.These results demonstrate the advantage of the proposed video stimulus paradigm and have significant theoretical and applied implications for developing VEP-based BCI systems.

Objective. Effective seizure prediction can reduce patient burden, improve clinical treatment accuracy, and lower healthcare costs. However, existing deep learning-based seizure prediction methods primarily rely on single models, which have limitations in feature extraction. This study aims to develop a hybrid model that integrates the advantages of convolutional neural networks (CNNs) and Transformer to enhance seizure prediction performance.Approach. We propose FusionXNet, a hybrid model inspired by CNNs and Transformer architectures, for seizure prediction. Specifically, we design a token synthesis unit to extract local features using convolution operations and capture global electroencephalography (EEG) representations via attention mechanisms. By merging local and global features extracted from the EEG segments, FusionXNet enhances feature representations, which are subsequently fed into a classifier for final seizure prediction.Main results. We evaluate the model on the publicly available Boston Children's Hospital and the Massachusetts Institute of Technology dataset, conducting segment-based and event-based experiments in both patient-specific and cross-patient settings. In event-based patient-specific experiments, FusionXNet achieves a sensitivity of 97.602% and a false positive rate (FPR) of 0.059 h-1. The results demonstrate that the proposed model effectively predicts seizures with high sensitivity and a low FPR, outperforming existing methods.Significance. The proposed FusionXNet model provides a robust and efficient approach for seizure prediction by leveraging both local and global feature extraction. The high sensitivity and low FPR indicate its potential for real-world clinical applications, improving patient management and reducing healthcare costs.

Objective.Elucidating neurological processes in the mammalian brain requires improved methods for imaging and detecting neuronal subtypes. Transgenic mouse models utilizing Cre/lox recombination have been developed to selectively label neuronal subtypes with fluorophores, however, light-scattering attenuation of both excitation light and emission light limits their effective range of detection.Approach. To overcome these limitations, this study investigates the use of a near-infrared fluorophore, iRFP713, for subtype labeling of neurons found within brain regions that are typically inaccessible by optical methods. Towards this goal, a custom photoacoustic (PA) system is developed for detection of iRFP in neurons in brain slices, expressed via Cre/lox, and withinin vitrocell culture.Main results. In this study, a custom system is developed to detect iRFP in neuronal cells both in brain slices andin vitro. Furthermore, this work validates iRFP expression in the brains of transgenic mice and neuronal cell culture.Significance. Combining iRFP with advanced imaging and detection strategies, such as PA microscopy, is critical for expanding the type and variety of neurons that scientists can observe within the mammalian brain.

Objective.Electroencephalography (EEG) decoding is challenging because of its temporal variability and low signal-to-noise ratio, which complicate the extraction of meaningful information from signals. Although convolutional neural networks (CNNs) effectively extract local features from EEG signals, they are constrained by restricted receptive fields. In contrast, transformers excel at capturing global dependencies through self-attention mechanisms but often require extensive training data and computational resources, which limits their efficiency on EEG datasets with limited samples.Approach.In this paper, we propose LGFormer, a hybrid network designed to efficiently learn both local and global representations for EEG decoding. LGFormer employs a deep attention module to extract global information from EEG signals, dynamically adjusting the focus of CNNs. Subsequently, LGFormer incorporates a local-enhanced transformer, combining the strengths of CNNs and transformers to achieve multiscale perception from local to global. Despite integrating multiple advanced techniques, LGFormer maintains a lightweight design and training efficiency.Main results.LGFormer achieves state-of-the-art performance within 200 training epochs across four public datasets, including motor imagery, cognitive workload, and error-related negativity decoding tasks. Additionally, we propose a novel spatial and temporal attention visualization method, revealing that LGFormer captures discriminative spatial and temporal features, enhancing model interpretability and providing insights into its decision-making process.Significance.In summary, LGFormer demonstrates superior performance while maintaining high training efficiency across different tasks, highlighting its potential as a versatile and practical model for EEG decoding.