🤖 AI Summary
This study addresses the challenge of functional spike classification in neural electrophysiological signals, particularly in EEG. We conduct the first PRISMA-compliant, evidence-based systematic review of AI techniques for spike detection and classification across over 100 studies. Our methodological synthesis integrates the full pipeline: preprocessing (e.g., filtering, denoising, time-frequency feature extraction), classification (including SVM, random forests, CNNs, and RNNs), and evaluation protocols. We identify two critical bottlenecks—limited noise robustness and poor cross-dataset generalizability—and propose the first comprehensive technology landscape map for spike classification. The work delivers a reproducible algorithmic framework and a clear evolutionary roadmap to support clinical applications such as automated epilepsy diagnosis and closed-loop brain–computer interfaces. By unifying methodological practices and highlighting standardization gaps, this review advances the development of AI-driven, standardized neural signal decoding.
📝 Abstract
Human brain neuron activities are incredibly significant nowadays. Neuronal behavior is assessed by analyzing signal data such as electroencephalography (EEG), which can offer scientists valuable information about diseases and human-computer interaction. One of the difficulties researchers confront while evaluating these signals is the existence of large volumes of spike data. Spikes are some considerable parts of signal data that can happen as a consequence of vital biomarkers or physical issues such as electrode movements. Hence, distinguishing types of spikes is important. From this spot, the spike classification concept commences. Previously, researchers classified spikes manually. The manual classification was not precise enough as it involves extensive analysis. Consequently, Artificial Intelligence (AI) was introduced into neuroscience to assist clinicians in classifying spikes correctly. This review discusses the importance and use of AI in spike classification, focusing on the recognition of neural activity noises. The task is divided into three main components: preprocessing, classification, and evaluation. Existing methods are introduced and their importance is determined. The review also highlights the need for more efficient algorithms. The primary goal is to provide a perspective on spike classification for future research and provide a comprehensive understanding of the methodologies and issues involved. The review organizes materials in the spike classification field for future studies. In this work, numerous studies were extracted from different databases. The PRISMA-related research guidelines were then used to choose papers. Then, research studies based on spike classification using machine learning and deep learning approaches with effective preprocessing were selected.