Electroencephalogram Channels Research Articles

Real-time dynamic analysis of EEG Response for Live Indian Classical Vocal Stimulus with Therapeutic Indications

Numerous studies have been conducted on the connection between music and the brain, and it has been established that listening to music directly affects brain activity and stimulation. The potential benefits of music therapy, which uses music as a tool for healing and fostering well-being, have come to light in a number of circumstances. However, there is a gap in understanding the effects of Indian classical music (ICM) on the brain and its therapeutic applications. Yaman and Puria Dhanashree were the two chosen ragas, which share same notes (swaras) and differs in two of their counter notes. The brain responses are captured from five volunteers through 24 channel Electroencephalogram (EEG) cap using a smartphone, which is utilized to allocate electrodes to different regions of the brain. In this work, different automated approaches for identifying brain regions evoked to live ICM stimuli are proposed, considering input and output uncertainties. These approaches are based on automated energy and Mahalanobis distance measurements, and, a region-specific real-time algorithm based on eigen perturbation, which provides a measure to capture the time evolution of brain activity. This identification is relevant in understanding dynamic changes in brain responses during musical experiences providing a more comprehensive perception and processing of ICM in the human brain. Also, significant change in beta band power reduction was observed after music. These approaches can help integrate it into evidence-based music therapy for cognitive, emotional, and psychological conditions. The findings of this study provide evidence indicating ragas activate different brain regions based on listener’s musical knowledge and is a first step for mhealth based applications.

Electroencephalogram-Based Metastability in Mild Cognitive Impairment Alzheimer's Disease.

Background: In this study, we analyze metastability, a feature of brain dynamics in subjects experiencing mild cognitive impairment Alzheimer's disease (MCI-AD) under eyes open (EO) and eyes closed (EC) conditions. Alzheimer's disease (AD) is a critically prolonged brain disorder that interrupts neural synchronization and desynchronization. Thus, studying metastability under EO and EC conditions would help in understanding the cortical dynamics and its impact in early-stage AD. Methods: Metastability is investigated using three methods namely frequency variance analysis, Kuramoto order parameter, and through meta-state activation patterns. Frequency variance estimated from 21 electroencephalogram (EEG) channels was clustered into three regions namely anterior, central, and posterior to study the regional metastability analysis. Global metastability was assessed from Kuramoto order parameter and meta-state activation patterns by collating the 21 EEG channels. Results: Reduction in metastability was observed in central regions of MCI-AD subjects through the study of frequency variance analysis. There was a marked reduction in global metastability in the patient group under the resting EO condition. Reduction in meta-state activation properties such as temporal activation sequence complexity, modularity, and leap size in MCI-AD condition under the EO condition indicates an overall reduction in brain flexibility. Conclusion: Taken together, the study infers an underlying structural change in neuronal dynamics influencing the reduction of metastability under the MCI-AD condition. The study further revealed that this reduction in metastability is more pronounced in the EO condition.

Optimization of epilepsy detection method based on dynamic EEG channel screening

To decrease the interference in the process of epileptic feature extraction caused by insufficient detection capability in partial channels of focal epilepsy, this paper proposes a novel epilepsy detection method based on dynamic electroencephalogram (EEG) channel screening. This method not only extracts more effective epilepsy features but also finds common features among different epilepsy subjects, providing an effective approach and theoretical support for across-subject epilepsy detection in clinical scenarios.Firstly, we use the Refine Composite Multiscale Dispersion Entropy (RCMDE) to measure the complexity of EEG signals between normal and seizure states and realize the dynamic EEG channel screening among different subjects, which can enhance the capability of feature extraction and the robustness of epilepsy detection. Subsequently, we discover common epilepsy features in 3–15 Hz among different subjects by the screened EEG channels. By this finding, we construct the Residual Convolutional Long Short-Term Memory (ResCon-LSTM) neural network to accomplish across-subject epilepsy detection.The experiment results on the CHB-MIT dataset indicate that the highest accuracy of epilepsy detection in the single-subject experiment is 98.523 %, improved by 5.298 % compared with non-channel screening. In the across-subject experiment, the average accuracy is 96.596 %. Therefore, this method could be effectively applied to different subjects by dynamically screening optimal channels and keep a good detection performance.

Hierarchical Dynamic Graph Convolutional Network With Interpretability for EEG-Based Emotion Recognition.

Graph convolutional networks (GCNs) have shown great prowess in learning topological relationships among electroencephalogram (EEG) channels for EEG-based emotion recognition. However, most existing GCN-only methods are designed with a single spatial pattern, lacking connectivity enhancement within local functional regions and ignoring the data dependencies of EEG original data. In this article, hierarchical dynamic GCN (HD-GCN) is proposed to explore dynamic multilevel spatial information among EEG channels, with discriminative features of EEG signals as auxiliary information. Specifically, representation learning in topological space consists of two branches: one for extracting global dynamic information and one for exploring augmentation information in local functional regions. In each branch, a layerwise adjacency matrix is utilized to enrich the expressive power of GCN. Furthermore, a data-dependent auxiliary information module (AIM) is developed to capture multidimensional fusion features. Extensive experiments on two public datasets, SJTU emotion EEG dataset (SEED) and DREAMER, demonstrate that the proposed method consistently exceeds state-of-the-art methods. Interpretability analysis of the proposed model is performed, discovering the active brain regions and important electrode pairs related to emotion.

A FUSION OF A DISCRETE WAVELET TRANSFORM-BASED AND TIME-DOMAIN FEATURES EXTRACTION FOR MOTOR IMAGERY CLASSIFICATION

A motor imagery (MI)-based brain-computer interface (BCI) has performed successfully as a control mechanism with multiple electroencephalogram (EEG) channels. For practicality, fewer EEG channels are preferable. This paper investigates a single-channel EEG signal for MI. However, there are insufficient features that can be extracted due to a single-channel EEG signal being used in one region of the brain. An effective feature extraction technique plays a critical role in overcoming this limitation. Therefore, this study proposes a fusion of discrete wavelet transform (DWT)-based and time-domain feature extraction to provide more relevant information for classification. The highest accuracy obtained on the BCI Competition III (IVa) dataset is 87.5% with logistic regression (LR) while the OpenBMI dataset attained the highest accuracy of 93% with support vector machine (SVM) as the classifier. Addressing the potential of enhancing the performance of a single EEG channel located on the forehead, the achieved result is relatively promising.

Correlation Between Sleep Continuity and Patient-Reported Sleep Quality in Conscious Critically Ill Patients at High Risk of Reintubation: A Pilot Study.

It is well-established that sleep quality of ICU patients is poor, with sleep being highly fragmented by multiple awakenings. These sleep disruptions are associated with poor outcomes such as prolonged weaning duration from mechanical ventilation. Polysomnography can measure sleep continuity, a parameter associated positively with outcomes in patients treated with noninvasive ventilation, but polysomnography is not routinely available in all ICUs, and simple means to assess sleep quality are needed. The Richards-Campbell sleep questionnaire (RCSQ) assesses sleep quality in ICU patients but is difficult to administrate in patients who are not fully awake, and a simpler sleep numeric rating scale (sleep-NRS) has been proposed as an alternative. We here investigated the relationships between sleep continuity and patients-reported sleep quality. Single-center retrospective study. Medical ICU of Poitiers University Hospital. Seventy-two patients were extubated from mechanical ventilation and at high risk of reintubation. None. We analyzed 52 previously recorded polysomnographies in nonsedated and conscious ICU patients. Sleep was recorded the night after extubation. Sleep continuity was measured using an automated scoring algorithm from one electroencephalogram (EEG) channel of the polysomnography. Patient-reported sleep quality was assessed using RCSQ and sleep-NRS. Sleep continuity could be calculated on 45 polysomnographies (age: 68 [58-77], median [25th-75th]) RCSQ (62 [48-72]) and sleep-NRS (6.0 [5.0-7.0]) were obtained in 21 patients and 34 patients, respectively. Our results show a significant correlation between sleep continuity and sleep-NRS (p = 0.0037; ρ = 0.4844; n = 34) but not with RCSQ score (p = 0.6732; ρ = 0.1005; n = 20). Sleep continuity correlates with patient-reported sleep quality assessed using sleep-NRS and may capture the refreshing part of sleep. Sleep-NRS can be easily administered in ICU patients. Sleep continuity and sleep-NRS are simple tools that may prove useful to evaluate sleep quality in ICU patients.

Fusing the spatial structure of electroencephalogram channels can increase the individualization of the functional connectivity network.

An electroencephalogram (EEG) functional connectivity (FC) network is individualized and plays a significant role in EEG-based person identification. Traditional FC networks are constructed by statistical dependence and correlation between EEG channels, without considering the spatial relationships between the channels. The individual identification algorithm based on traditional FC networks is sensitive to the integrity of channels and crucially relies on signal preprocessing; therefore, finding a new presentation for FC networks may help increase the performance of the identification algorithms. EEG signals are smooth across space owing to the volume conduction effect. Considering such spatial relationships among channels can provide a more accurate representation of FC networks. In this study, we propose an EEG FC network with virtual nodes that combines the spatial relationships and functional connectivity of channels. The comparison results for individual identification show that the novel EEG network is more individualized and achieves an accuracy of 98.64% for data without preprocessing. Furthermore, our algorithm is more robust in reducing the number of channels and can perform well even when a large area of channels is removed.

SGCRNN: A ChebNet-GRU fusion model for eeg emotion recognition

The paper proposes a deep learning model based on Chebyshev Network Gated Recurrent Units, which is called Spectral Graph Convolution Recurrent Neural Network, for multichannel electroencephalogram emotion recognition. First, in this paper, an adjacency matrix capturing the local relationships among electroencephalogram channels is established based on the cosine similarity of the spatial locations of electroencephalogram electrodes. The training efficiency is improved by utilizing the computational speed of the cosine distance. This advantage enables our method to have the potential for real-time emotion recognition, allowing for fast and accurate emotion classification in real-time application scenarios. Secondly, the spatial and temporal dependence of the Spectral Graph Convolution Recurrent Neural Network for capturing electroencephalogram sequences is established based on the characteristics of the Chebyshev network and Gated Recurrent Units to extract the spatial and temporal features of electroencephalogram sequences. The proposed model was tested on the publicly accessible dataset DEAP. Its average recognition accuracy is 88%, 89.5%, and 89.7% for valence, arousal, and dominance, respectively. The experiment results demonstrated that the Spectral Graph Convolution Recurrent Neural Network method performed better than current models for electroencephalogram emotion identification. This model has broad applicability and holds potential for use in real-time emotion recognition scenarios.

EEG cortical sources of face and word as distractors during emotional interference

Objectives: Faces and words bearing emotional valence are commonly encountered affective stimuli by humans. It is known that emotional distractors interfere with the task goal and disrupt the cognitive performance. However, the differential cortical sources involved as neural substrates, to process emotional distractors, while affecting performance during the presence of another emotional stimuli, (emotional interference) is still unexplored. Materials and Methods: Emotional interference was studied in 20 healthy adults (25.15 ± 2.978) while performing face-word interference (FwI) and Word-face interference (WfI) tasks, wherein words with emotions and faces were distractors and 128 channel electroencephalogram was acquired simultaneously. Further, standardised low-resolution electromagnetic tomography-based source analysis was done to study the differences in the cortical activity between the tasks across 66 gyri. Results: Higher reaction time (RT) and lower accuracy percentage were seen for categorising face in presence of word (FwI) versus word in presence of face (WfI) (P < 0.001). We observed no difference in RT and lower accuracy percentage between incongruent FWI trials as compared to congruent FWI trials. In addition, we observed higher RT and lower accuracy percentage between incongruent WFI trials as compared to congruent WFI trials. Source analysis revealed higher cortical activity across 60 gyri and significantly lower cortical activity across three gyri during performance of FwI compared to WfI task (P < 0.05/66). Activation of areas associated with saliency, conflict and goal directed behaviour exhibited consistently higher activity in FwI trials compared to WfI trials indicating the robustness of face processing and the hierarchical interplay of neural substrates in conditions of cognitive control. Conclusion: Cortical processing of face emerged as more potent distractor compared to an emotional word during emotional interference task.

Domain knowledge-assisted multi-objective evolutionary algorithm for channel selection in brain-computer interface systems.

For non-invasive brain-computer interface systems (BCIs) with multiple electroencephalogram (EEG) channels, the key factor limiting their convenient application in the real world is how to perform reasonable channel selection while ensuring task accuracy, which can be modeled as a multi-objective optimization problem. Therefore, this paper proposed a two-objective problem model for the channel selection problem and introduced a domain knowledge-assisted multi-objective optimization algorithm (DK-MOEA) to solve the aforementioned problem. The multi-objective optimization problem model was designed based on the channel connectivity matrix and comprises two objectives: one is the task accuracy and the other one can sensitively indicate the removal status of channels in BCIs. The proposed DK-MOEA adopted a two-space framework, consisting of the population space and the knowledge space. Furthermore, a knowledge-assisted update operator was introduced to enhance the search efficiency of the population space by leveraging the domain knowledge stored in the knowledge space. The proposed two-objective problem model and DK-MOEA were tested on a fatigue detection task and four state-of-the-art multi-objective evolutionary algorithms were used for comparison. The experimental results indicated that the proposed algorithm achieved the best results among all the comparative algorithms for most cases by the Wilcoxon rank sum test at a significance level of 0.05. DK-MOEA was also compared with a version without the utilization of domain knowledge and the experimental results validated the effectiveness of the knowledge-assisted mutation operator. Moreover, the comparison between DK-MOEA and a traditional classification algorithm using all channels demonstrated that DK-MOEA can strike the balance between task accuracy and the number of selected channels. The formulated two-objective optimization model enabled the selection of a minimal number of channels without compromising classification accuracy. The utilization of domain knowledge improved the performance of DK-MOEA. By adopting the proposed two-objective problem model and DK-MOEA, a balance can be achieved between the number of the selected channels and the accuracy of the fatigue detection task. The methods proposed in this paper can reduce the complexity of subsequent data processing and enhance the convenience of practical applications.

A channel selection method to find the role of the amygdala in emotion recognition avoiding conflict learning in EEG signals

Emotion recognition using electroencephalogram signals has been widely studied in the last decade, achieving artificial intelligence models that accurately classify primitive or primary emotions. However, most of these models focus on signal processing methods to better recognize multiclass targets, ignoring efficient denoising methods to reduce artifacts in input samples. Therefore, this study proposes two-dimension reduction algorithms derived from machine learning models based on electroencephalogram channel selection and conflict learning. The last two approaches exploit the electroencephalogram signals from the SEED-V dataset as input data. Next, a wavelet noise-estimate frequency decomposition and a 1-D Local Binary Pattern (LBP) are applied to achieve a histogram per signal. After applying a feature extraction method, the targets per sample are adapted to yield the most relevant electroencephalogram channels and obtain a highly competitive machine-learning model that uses only the FCZ and CP4 electrodes. Additionally, relevant findings based on conflict learning yield that samples with “Happy” and “Disgust” targets had numerous artifacts compared with the other studied emotions (“Fear”, “Sad”, and “Neutral) but achieved superior performance than the channel selection method. The proposed framework reached accuracy rates per dimension reduction near 90% accuracy and between 87% and 92.8% using the F1-score metric. Hence, the classification results are highly competitive with state-of-the-art close-related methods.

EEG-based sleep staging via self-attention based capsule network with Bi-LSTM model

Sleep staging via electroencephalogram is essential for determining the quality of sleep. Manual sleep staging is expensive and time-consuming. Recently, many deep learning-based sleep staging methods are demonstrated to outperform traditional methods. However, most methods do not fully exploit the temporal correlation between features of electroencephalogram signals. In this paper, we propose a self-attention routing-based capsule network with bi-directional long short-term memory model to extract more discriminative features from electroencephalogram signals and improve the accuracy of sleep staging. First, a convolutional neural network is used to extract salient features from the electroencephalogram signal. Second, to learn the transition rules between different sleep epochs, a bi-directional long short-term memory is used to capture the temporal dependence between the encoded electroencephalogram signals. Finally, to fully explore the temporal correlation between the features from the electroencephalogram signals, a self-attention routing-based capsule network is utilized to recode the importance based on the intrinsic temporal similarity of electroencephalogram signals. We evaluated our model by two different single-channel electroencephalogram signals (i.e., Fpz-Cz and Pz-Oz electroencephalogram channels) from two public sleep datasets, named Sleep-EDF-39 and Sleep-EDF-153. Our overall accuracies on the Sleep-EDF-39 and Sleep-EDF-153 datasets are 85.8% and 83.4%, with a kappa of 0.8 and 0.77, respectively. The results show that our proposed method achieves the state-of-the-art level of sleep staging using a single-channel electroencephalogram and offers the possibility of widespread application of capsule networks for sleep staging.

Comparative analysis of interchannel interactions on electroencephalograms in healthy people and patients with depression using the method of correlation ratios

In the article, based on clinical material obtained in the course of a study of patients with depression, an approach was applied to the analysis of the dependences of electroencephalogram channels using correlation ratios. It can objectively evaluate the properties of connections in any of their models (linear and non-linear). On the basis of the constructed matrices of correlation ratios, the possibility of analyzing paired dependences is shown in order to determination of parameters that differ for the norm and pathology. The direction of the connections between the leads was also established. In our opinion, differences in interhemispheric occipital connections are the most significant. This approach could become the basis for determining unique quantitative markers of depressive states and other neuropsychiatric disorders.

A Transfer entropy-based methodology to analyze information flow under eyes-open and eyes-closed conditions with a clinical perspective

Studying brain dynamics under normal or pathological conditions has proven to be a challenging task, as there is no unified consensus on the best approach. In this article, we present a methodology based on Transfer Entropy to study the information flow between different brain hemispheres in healthy subjects during eyes-open (EO) and eyes-closed (EC) resting states. We used an experimental setup that mimics the technical conditions found in clinical settings and collected data sets from short records of 24 channels electroencephalogram (EEG) at a sampling rate of 65 Hz. Our methodology accounts for interhemispheric and intrahemispheric information flow analysis in both conditions and relies on 4 indexes calculated from the transfer entropy estimations between EEG channels. These indexes provide information on the number, strength, and directionality of active connections. Our results suggest an increase in information transfer in the EC condition for the alpha, beta1, and beta2 frequency bands, but no preferred direction of interhemispheric information movement under either condition. These results are consistent with previously reported studies conducted with denser EEG recordings sampled at a higher rate. In conclusion, our methodology shows a significant difference in the brain’s dynamics of information transfer between EO and EC resting states, which can also be applied to regular clinical sessions.

Automatic sleep staging model based on single channel electroencephalogram signal

Sleep staging is the basis for solving sleep problems. There's an upper limit for the classification accuracy of sleep staging models based on single-channel electroencephalogram (EEG) data and features. To address this problem, this paper proposed an automatic sleep staging model that mixes deep convolutional neural network (DCNN) and bi-directional long short-term memory network (BiLSTM). The model used DCNN to automatically learn the time-frequency domain features of EEG signals, and used BiLSTM to extract the temporal features between the data, fully exploiting the feature information contained in the data to improve the accuracy of automatic sleep staging. At the same time, noise reduction techniques and adaptive synthetic sampling were used to reduce the impact of signal noise and unbalanced data sets on model performance. In this paper, experiments were conducted using the Sleep-European Data Format Database Expanded and the Shanghai Mental Health Center Sleep Database, and achieved an overall accuracy rate of 86.9% and 88.9% respectively. When compared with the basic network model, all the experimental results outperformed the basic network, further demonstrating the validity of this paper's model, which can provide a reference for the construction of a home sleep monitoring system based on single-channel EEG signals.

Design and implementation of integrated high-speed wireless EEG/ECoG acquisition system

With the development and application of Brain-Computer Interface (BCI), the demand for the portability, transmission performance, sampling rate, and number of channels of electroencephalogram (EEG) acquisition devices is increasing. The high-performance acquisition devices used in current research are generally large in size which limit its application scenarios, while portable high-performance acquisition devices have a limited wireless transmission bandwidth. To tackle these issues, a high-performance multichannel wireless EEG acquisition system was designed, the acquisition device is powered independently by a battery, controls the bioelectric acquisition chip through FPGA (filed-programmable gate array) to achieve high-speed and stable processing of EEG data, and transmits it in real-time via Wi-Fi to the host computer for display and recording. Standard signal source tests and Electrocorticography (ECoG) acquisition experiments demonstrate that the system performed well with 64 channels and a maximum sampling rate of 30 kHz within a distance of 10 meters. This design provides a new solution for neuroscience research scenarios.

Sparse Logistic Regression-Based EEG Channel Optimization Algorithm for Improved Universality across Participants

Electroencephalogram (EEG) channel optimization can reduce redundant information and improve EEG decoding accuracy by selecting the most informative channels. This article aims to investigate the universality regarding EEG channel optimization in terms of how well the selected EEG channels can be generalized to different participants. In particular, this study proposes a sparse logistic regression (SLR)-based EEG channel optimization algorithm using a non-zero model parameter ranking method. The proposed channel optimization algorithm was evaluated in both individual analysis and group analysis using the raw EEG data, compared with the conventional channel selection method based on the correlation coefficients (CCS). The experimental results demonstrate that the SLR-based EEG channel optimization algorithm not only filters out most redundant channels (filters 75–96.9% of channels) with a 1.65–5.1% increase in decoding accuracy, but it can also achieve a satisfactory level of decoding accuracy in the group analysis by employing only a few (2–15) common EEG electrodes, even for different participants. The proposed channel optimization algorithm can realize better universality for EEG decoding, which can reduce the burden of EEG data acquisition and enhance the real-world application of EEG-based brain–computer interface (BCI).

Association Between Risk of Stroke and Delirium After Cardiac Surgery and a New Electroencephalogram Index of Interhemispheric Similarity

Neurologic complications after surgery (stroke, delirium) remain a major concern despite advancements in surgical and anesthetic techniques. The authors aimed to evaluate whether a novel index of interhemispheric similarity, the lateral interconnection ratio (LIR), between 2 prefrontal electroencephalogram (EEG) channels could be associated with stroke and delirium following cardiac surgery. Retrospective observational study. Single university hospital. A total of 803 adult patients without documentation of a previous stroke, who underwent cardiac surgery with cardiopulmonary bypass (CPB) between July 2016 and January 2018. The LIR index was calculated retrospectively from the patients' EEG database. LIR was analyzed intraoperatively every 10 seconds and compared among patients with postoperative stroke, patients with delirium, and patients without documented neurologic complications, during 5 key periods, each lasting10 minutes: (1) surgery start, (2) before CPB, (3) on CPB, (4) after CPB, and (5) surgery end. After cardiac surgery, 31 patients suffered from stroke; 48 patients were diagnosed with delirium; and 724 had no documented neurologic complications. Patients with stroke demonstrated a decrease in LIR index between the start of surgery and the postbypass period of 0.08 (0.01, 0.36 [21]; median and [interquartile range {IQR}]; valid EEG samples); whereas there was no similar decrease in the no-dysfunction group (-0.04 [-0.13, 0.04; {551}], p < 0.0001). Patients with delirium showed a decrease in LIR index between the start of surgery and the end of the surgery by 0.15 (0.02, 0.30 [12]), compared with no such decrease in the no-dysfunction group (-0.02 [-0.12, 0.08 {376}], p ≈ 0.001). After improvement of SNR, it might be of value to further study the index decrease as an indication for risk for brain injury after surgery. The timing of decrease (after CPB or end of surgery) may provide hints regarding the injury pathophysiology and its onset.

Design of a Sleep Modulation System with FPGA-Accelerated Deep Learning for Closed-loop Stage-Specific In-Phase Auditory Stimulation.

Closed-loop sleep modulation is an emerging research paradigm to treat sleep disorders and enhance sleep benefits. However, two major barriers hinder the widespread application of this research paradigm. First, subjects often need to be wire-connected to rack-mount instrumentation for data acquisition, which negatively affects sleep quality. Second, conventional real-time sleep stage classification algorithms give limited performance. In this work, we conquer these two limitations by developing a sleep modulation system that supports closed-loop operations on the device. Sleep stage classification is performed using a lightweight deep learning (DL) model accelerated by a low-power field-programmable gate array (FPGA) device. The DL model uses a single channel electroencephalogram (EEG) as input. Two convolutional neural networks (CNNs) are used to capture general and detailed features, and a bidirectional long-short-term memory (LSTM) network is used to capture time-variant sequence features. An 8-bit quantization is used to reduce the computational cost without compromising performance. The DL model has been validated using a public sleep database containing 81 subjects, achieving a state-of-the-art classification accuracy of 85.8% and a F1-score of 79%. The developed model has also shown the potential to be generalized to different channels and input data lengths. Closed-loop in-phase auditory stimulation has been demonstrated on the test bench.

Seizure detection with reduced electroencephalogram channels: research trends and outlook.

Epilepsy is a prevalent condition characterized by recurrent, unpredictable seizures. Monitoring with surface electroencephalography (EEG) is the gold standard for diagnosing epilepsy, but a time-consuming, uncomfortable and sometimes ineffective process for patients. Further, using EEG over a brief monitoring period has variable success, dependent on patient tolerance and seizure frequency. The availability of hospital resources and hardware and software specifications inherently restrict the options for comfortable, long-term data collection, resulting in limited data for training machine-learning models. This mini-review examines the current patient journey, providing an overview of the current state of EEG monitoring with reduced electrodes and automated channel reduction methods. Opportunities for improving data reliability through multi-modal data fusion are suggested. We assert the need for further research in electrode reduction to advance brain monitoring solutions towards portable, reliable devices that simultaneously offer patient comfort, perform ultra-long-term monitoring and expedite the diagnosis process.