Brain-computer interfaces (BCIs) offer a groundbreaking approach to connecting the brain with external devices, bypassing damaged peripheral nerves and muscles. Electroencephalography (EEG)-based BCIs, being noninvasive, hold significant promise for clinical applications. These applications range from stroke rehabilitation and providing communication and control options for patients with impaired eye movements or vision to aiding in the prognosis of patients with cognitive-motor dissociation. However, clinical implementation faces challenges and limitations, including achieving high accuracy and addressing the issue of limited data availability for training robust and reliable BCI systems. This review focuses on addressing these challenges by examining recent advancements in BCI technology and proposing solutions to improve their performance and clinical applicability. The study aims to summarize the advancements in BCI technology over the past decade and identify key trends, challenges, and potential solutions for their broader adoption in clinical settings. A critical aspect will be the exploration of methods to improve classification accuracy, especially in online settings where real-time performance is crucial.

The review examined papers published between 2011 and 2021 with over 100 citations in the Web of Science database. The selection criteria included keywords such as "brain-computer interface" and focused on articles rather than proceedings or reviews. The papers reviewed represent a progression in BCI technology from simpler statistical models to more sophisticated deep learning approaches. Early studies focused on embedded systems for signal acquisition and processing, utilizing techniques like FFT and SLIC for feature extraction and linear regression or simple classifiers for translating brain activity into commands. Later studies incorporated machine learning models such as Support Vector Machines (SVMs) to improve classification accuracy. The most recent research leverages deep learning techniques, including Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTMs), achieving significantly higher accuracy in classifying motor imagery tasks. This evolution reflects advancements in computing power and the development of more powerful algorithms.

This mini-review employed a systematic literature review methodology. The researchers utilized the Web of Science database to identify articles published between January 2011 and January 2021, focusing on those cited more than 100 times. Search terms included variations of "brain-computer interface." The selection process prioritized original research articles, excluding reviews and conference proceedings. The primary performance metric considered was classification accuracy, with a threshold of >75% defined as successful, and <70% deemed unacceptable. Both offline and online validation approaches were considered when assessing the performance of the BCI systems. Offline validation uses pre-collected datasets, while online validation tests real-time performance using data collected directly from subjects during BCI operation. The review systematically analyzes the performance, methodology, and limitations of each study, highlighting advancements and persistent challenges in the field.

The review reveals a significant improvement in BCI performance over the last decade, largely due to the adoption of machine learning and, more recently, deep learning models. Early studies using linear regression and basic feature extraction methods achieved accuracies around 70%. The introduction of Support Vector Machines (SVMs) led to improvements, reaching accuracies around 80%. The most significant leap came with the application of deep learning, specifically CNNs and LSTMs, which resulted in accuracies exceeding 90% in some offline studies. However, a substantial gap exists between offline and online performance, with online accuracy consistently lower by 20-50%. This discrepancy highlights the need for robust data augmentation and more sophisticated algorithms that can generalize well to real-world, noisy conditions. Several studies explored data augmentation techniques, including generative adversarial networks (GANs), to address the problem of limited training data in medical BCIs. GAN-based augmentation significantly improved classification accuracy in several applications. For instance, the cDCGAN method improved motor imagery classification accuracy from 82.8% to 85.8%, while sWGAN improved emotion recognition accuracy from 83.3% to 92.2%. The study also noted the limitations of existing BCIs, such as high cost, lack of portability, and the need for individual training and parameter tuning for specific tasks. The study identified a significant performance difference between offline and online evaluations of BCI systems, with a notable drop in online accuracy attributed to the limited data available for training robust and reliable classifiers.

The findings highlight the impressive progress in BCI technology over the past decade. The transition from simple statistical methods to advanced deep learning models has significantly enhanced the accuracy and capabilities of BCIs. The consistent gap between offline and online performance, however, underscores the critical need for more robust algorithms and data augmentation techniques. The success of GAN-based data augmentation demonstrates the potential of this approach to overcome the limitations imposed by the scarcity of training data in medical BCIs. The lower online accuracy compared to offline accuracy points to the challenge of dealing with real-world noise and variability in EEG signals. Future research should prioritize the development of more robust algorithms that are less sensitive to noise and variations in individual EEG characteristics. The lack of versatility in existing BCIs, requiring specific training for each task, poses a significant limitation. The review suggests that future research should explore more generalizable models capable of adapting to various tasks, thereby enhancing the clinical utility of BCIs.

This review demonstrates substantial progress in noninvasive BCI technology, particularly in accuracy driven by deep learning. However, the persistent gap between offline and online performance, coupled with a lack of versatility, necessitates further research. Data augmentation techniques, such as GANs, prove promising, but robust, generalized models capable of handling real-world noise and diverse clinical applications are crucial for future advancement. Exploring theories like the Global Workspace Theory may pave the way for more versatile and adaptable BCIs.

The review is limited by the scope of the literature search, focusing primarily on highly cited articles. It may not fully capture all relevant advancements in the field. The evaluation of BCI performance relies heavily on classification accuracy, potentially overlooking other critical aspects such as latency and robustness. The generalization of findings from specific studies might be limited given the diverse nature of BCI applications and the variability in experimental designs. Finally, the review focuses on a specific time period and may not fully represent the ongoing rapid advancements in the field.