The Tenth International brain-computer interface (BCI) meeting was held June 6-9, 2023, in the Sonian Forest in Brussels, Belgium. At that meeting, 21 master classes, organized by the BCI Society's Postdoc & Student Committee, supported the Society's goal of fostering learning opportunities and meaningful interactions for trainees in BCI-related fields. Master classes provide an informal environment where senior researchers can give constructive feedback to the trainee on their chosen and specific pursuit. The topics of the master classes span the whole gamut of BCI research and techniques. These include data acquisition, neural decoding and analysis, invasive and noninvasive stimulation, and ethical and transitional considerations. Additionally, master classes spotlight innovations in BCI research. Herein, we discuss what was presented within the master classes by highlighting each trainee and expert researcher, providing relevant background information and results from each presentation, and summarizing discussion and references for further study.

We introduce a novel approach to training data augmentation in brain-computer interfaces (BCIs) using neural field theory (NFT) applied to EEG data from motor imagery tasks. BCIs often suffer from limited accuracy due to a limited amount of training data. To address this, we leveraged a corticothalamic NFT model to generate artificial EEG time series as supplemental training data. We employed the BCI competition IV '2a' dataset to evaluate this augmentation technique. For each individual, we fitted the model to common spatial patterns of each motor imagery class, jittered the fitted parameters, and generated time series for data augmentation. Our method led to significant accuracy improvements of over 2% in classifying the "total power" feature, but not in the case of the "Higuchi fractal dimension" feature. This suggests that the fit NFT model may more favorably represent one feature than the other. These findings pave the way for further exploration of NFT-based data augmentation, highlighting the benefits of biophysically accurate artificial data.

Disorders of consciousness (DoC) pose significant challenges in neurology. Conventional neuromodulation therapies for DoC have exhibited limited success, with varying effectiveness among patients. In this study, we introduce a computational approach for constructing personalized stimulus signals capable of inducing healthy-like neural activity patterns in DoC patients. Leveraging a simplified brain model based on neural field theory, we fit this model to the power spectrum of a patient with DoC and derive a personalized stimulus time series to induce a healthy-like power spectrum. By applying this stimulus to brain regions typically targeted by stimulation therapies such as deep brain stimulation and repetitive transcranial magnetic stimulation, we demonstrate in silico, the ability of our method to elicit EEG power spectra resembling those of healthy individuals. We speculate that in the course of a long-term treatment, when the brain produces healthy-like activity, it may trigger intrinsic plasticity mechanisms, potentially leading to sustained improvements in the patient's condition. While further clinical adjustments and validation are needed, this novel approach offers promise in tailoring brain stimulation therapies for DoC patients. Moreover, it presents potential extensions to other conditions that could also benefit from brain stimulation therapies.### Competing Interest StatementThe authors have declared no competing interest.

Understanding the neural correlates of consciousness remains a central challenge in neuroscience. In this study, we explore the potential of neural field theory (NFT) as a computational framework for representing consciousness states. While prior research has validated NFT's capacity to differentiate between normal and pathological states of consciousness, the relationship of its parameters to the representation of consciousness levels remains unclear. Here, we fitted a corticothalamic NFT model to EEG data collected from healthy individuals and patients with disorders of consciousness. We then comprehensively explored the correlations between the fitted NFT parameters and features extracted from both experimental and simulated EEG data, across various states of consciousness. The identified correlations not only highlight the model's ability to differentiate between states of consciousness, but also shed light on the physiological bases of these states, pinpointing potential biomarkers. Our results provide valuable insights into how consciousness levels are represented within the NFT framework and into the dynamics of brain activity across various consciousness states. This underscores the potential of NFT as a useful tool for consciousness research, facilitating in-silico experimentation. ### Competing Interest Statement The authors have declared no competing interest.

Learned associations between stimuli allow us to model the world and make predictions, crucial for efficient behavior (e.g., hearing a siren, we expect to see an ambulance and quickly make way). While there are theoretical and computational frameworks for prediction, the circuit and receptor-level mechanisms are unclear. Using high-density EEG, Bayesian modeling, and machine learning, we show that inferred “causal” relationships between stimuli and frontal alpha activity account for reaction times (a proxy for predictions) on a trial-by-trial basis in an audiovisual delayed match-to-sample task which elicited predictions. Predictive β feedback activated sensory representations in advance of predicted stimuli. Low-dose ketamine, an NMDAR blocker, but not the control drug dexmedetomidine, perturbed behavioral indices of predictions, their representation in higher-order cortex, feedback to posterior cortex, and pre-activation of sensory templates in higher-order sensory cortex. This study suggests that predictions depend on alpha activity in higher-order cortex, β feedback, and NMDARs, and ketamine blocks access to learned predictive information. SIGNIFICANCE STATEMENT We learn the statistical regularities around us, creating associations between sensory stimuli. These associations can be exploited by generating predictions, which enable fast and efficient behavior. When predictions are perturbed, it can negatively influence perception and even contribute to psychiatric disorders, such as schizophrenia. Here we show that the frontal lobe generates predictions and sends them to posterior brain areas, to activate representations of predicted sensory stimuli before their appearance. Oscillations in neural activity (α and β waves) are vital for these predictive mechanisms. The drug ketamine blocks predictions and the underlying mechanisms. This suggests that the generation of predictions in the frontal lobe, and the feedback pre-activating sensory representations in advance of stimuli, depend on NMDARs.

Learned associations between stimuli allow us to model the world and make predictions, crucial for efficient behavior; e.g., hearing a siren, we expect to see an ambulance and quickly make way. While theoretical and computational frameworks for prediction exist, circuit and receptor-level mechanisms are unclear. Using high-density EEG and Bayesian modeling, we show that trial history and frontal alpha activity account for reaction times (a proxy for predictions) on a trial-by-trial basis in an audio-visual prediction task. Low-dose ketamine, a NMDA receptor blocker – but not the control drug dexmedetomidine – perturbed predictions, their representation in frontal cortex, and feedback to posterior cortex. This study suggests predictions depend on frontal alpha activity and NMDA receptors, and ketamine blocks access to learned predictive information.

Microbes interact with the world around them at the chemical level. However, directly examining the chemical exchange between microbes, and microbes and their environment, at ecological scales, i.e. the scale of a single bacterial cell or small groups of cells, remains a key challenge. Here we address this obstacle by presenting a methodology that enables Matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry (IMS) of bacterial microcolonies. By combining optimized sample preparation with sub-atmospheric pressure MALDI, we demonstrate that chemical output from groups of as few as ~50 cells can be visualized with MALDI-IMS. Application of this methodology to Bacillus subtilis and Streptomyces coelicolor revealed heterogeneity in chemical output across microcolonies, and asymmetrical metabolite production when cells grew within physiological gradients produced by Medicago sativa roots. Taken together, these results indicate that MALDI-IMS can readily visualize metabolites made by very small assemblages of bacterial cells, and that even these small groups of cells can differentially produce metabolites in response to local chemical gradients.

Microbes interact with the world around them at the chemical level. However, directly examining the chemical exchange between microbes and microbes and their environment, at ecological scales, i.e., the scale of a single bacterial cell or small groups of cells, remains a key challenge. Here we address this obstacle by presenting a methodology that enables matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry (IMS) of bacterial microcolonies. By combining optimized sample preparation with subatmospheric pressure MALDI, we demonstrate that chemical output from groups of as few as ∼50 cells can be visualized with MALDI-IMS. Application of this methodology to Bacillus subtilis and Streptomyces coelicolor revealed heterogeneity in chemical output across microcolonies and asymmetrical metabolite production when cells grew within physiological gradients produced by Medicago sativa roots. Taken together, these results indicate that MALDI-IMS can readily visualize metabolites made by very small assemblages of bacterial cells and that even these small groups of cells can differentially produce metabolites in response to local chemical gradients.

Sleep and anesthesia entail alterations in conscious experience. Conscious experience may be absent (unconsciousness) or take the form of dreaming, a state in which sensory stimuli are not incorporated into conscious experience (disconnected consciousness). Recent work has identified features of cortical activity that distinguish conscious from unconscious states; however, less is known about how cortical activity differs between disconnected states and normal wakefulness. We employed transcranial magnetic stimulation–electroencephalography (TMS–EEG) over parietal regions across states of anesthesia and sleep to assess whether evoked oscillatory activity differed in disconnected states. We hypothesized that alpha activity, which may regulate perception of sensory stimuli, is altered in the disconnected states of rapid eye movement (REM) sleep and ketamine anesthesia. Compared to wakefulness, evoked alpha power (8–12 Hz) was decreased during disconnected consciousness. In contrast, in unconscious states of propofol anesthesia and non-REM (NREM) sleep, evoked low-gamma power (30–40 Hz) was decreased compared to wakefulness or states of disconnected consciousness. These findings were confirmed in subjects in which dream reports were obtained following serial awakenings from NREM sleep. By examining signatures of evoked cortical activity across conscious states, we identified novel evidence that suppression of evoked alpha activity may represent a promising marker of sensory disconnection.