# EEG-based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies on Signal Sensing Technologies and Computational Intelligence Approaches and their Applications

arXiv: https://arxiv.org/abs/2001.11337 · HTML: https://mlsanity.com/paper/2001.11337/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jan 2020.

## Built on by (most cited first, 21 of 21)

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- [A Comparative Study of Conventional and Tripolar EEG for High-Performance Reach-to-Grasp BCI Systems](https://mlsanity.com/paper/2402.09448/) (2024)
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- [Unsupervised Pairwise Learning Optimization Framework for Cross-Corpus EEG-Based Emotion Recognition Based on Prototype Representation](https://mlsanity.com/paper/2508.11663/) (2025)
- [A Human-Machine Joint Learning Framework to Boost Endogenous BCI Training](https://mlsanity.com/paper/2309.03209/) (2023)
- [SCVCNet: Sliding cross-vector convolution network for cross-task and inter-individual-set EEG-based cognitive workload recognition](https://mlsanity.com/paper/2310.03749/) (2023)
- [Brain-inspired Computing Based on Deep Learning for Human-computer Interaction: A Review](https://mlsanity.com/paper/2312.07213/) (2023)
- [SSDLabeler: Realistic semi-synthetic data generation for multi-label artifact classification in EEG](https://mlsanity.com/paper/2512.05500/) (2025)
- [Adaptive Semi-Supervised Training of P300 ERP-BCI Speller System with Minimum Calibration Effort](https://mlsanity.com/paper/2602.15955/) (2026)
- [Atoms of Thought: Universal EEG Representation Learning with Microstates](https://mlsanity.com/paper/2605.20182/) (2026)

## Abstract (arXiv)

Brain-Computer Interface (BCI) is a powerful communication tool between users and systems, which enhances the capability of the human brain in communicating and interacting with the environment directly. Advances in neuroscience and computer science in the past decades have led to exciting developments in BCI, thereby making BCI a top interdisciplinary research area in computational neuroscience and intelligence. Recent technological advances such as wearable sensing devices, real-time data streaming, machine learning, and deep learning approaches have increased interest in electroencephalographic (EEG) based BCI for translational and healthcare applications. Many people benefit from EEG-based BCIs, which facilitate continuous monitoring of fluctuations in cognitive states under monotonous tasks in the workplace or at home. In this study, we survey the recent literature of EEG signal sensing technologies and computational intelligence approaches in BCI applications, compensated for the gaps in the systematic summary of the past five years (2015-2019). In specific, we first review the current status of BCI and its significant obstacles. Then, we present advanced signal sensing and enhancement technologies to collect and clean EEG signals, respectively. Furthermore, we demonstrate state-of-art computational intelligence techniques, including interpretable fuzzy models, transfer learning, deep learning, and combinations, to monitor, maintain, or track human cognitive states and operating performance in prevalent applications. Finally, we deliver a couple of innovative BCI-inspired healthcare applications and discuss some future research directions in EEG-based BCIs.