Eye movement classification, particularly the identification of fixations and saccades, plays a vital role in advancing our understanding of neurological functions and cognitive processing. Conventional modalities of data, such as RGB webcams, often face limitations such as motion blur, latency and susceptibility to noise. Neuromorphic Vision Sensors, also known as event cameras (ECs), capture pixel-level changes asynchronously and at a high temporal resolution, making them well suited for detecting the swift transitions inherent to eye movements. However, the resulting data are sparse, which makes them less well suited for use with conventional algorithms. Spiking Neural Networks (SNNs) are gaining attention due to their discrete spatio-temporal spike mechanism ideally suited for sparse data. These networks offer a biologically inspired computational paradigm capable of modeling the temporal dynamics captured by event cameras. This study validates the use of Spiking Neural Networks (SNNs) with event cameras for efficient eye movement classification. We manually annotated the EV-Eye dataset, the largest publicly available event-based eye-tracking benchmark, into sequences of saccades and fixations, and we propose a convolutional SNN architecture operating directly on spike streams. Our model achieves an accuracy of 94% and a precision of 0.92 across annotated data from 10 users. As the first work to apply SNNs to eye movement classification using event data, we benchmark our approach against spiking baselines such as SpikingVGG and SpikingDenseNet, and additionally provide a detailed computational complexity comparison between SNN and ANN counterparts. Our results highlight the efficiency and robustness of SNNs for event-based vision tasks, with over one order of magnitude improvement in computational efficiency, with implications for fast and low-power neurocognitive diagnostic systems.

Microsaccades – rapid, fixational eye movements typically smaller than 1° – play a crucial role in maintaining visual stability and are increasingly recognized as indicators of internal neurological states. Classical studies and recent evidence indicate that microsaccade timing and rate are closely linked to attentional shifts and task demands, highlighting their functional significance. However, methodological inconsistencies in definitions and detection parameters continue to challenge interpretation. Converging findings across behavioral, neural, and computational studies support the use of microsaccades as precise, noninvasive probes of perception, attention, and cognition. This review synthesizes decades of research along four main dimensions: the kinematics and definition of microsaccades, their neural control mechanisms, links to cognitive and affective processes, and methods for their detection and analysis, including emerging high-speed and event-based sensors. We also identify open challenges, including the need for standardized criteria, robust model-based detection at kilohertz rates, and advanced event-based tracking. Addressing these challenges will enable microsaccades to serve as a reliable window into the dynamics of neural and cognitive processes. We conducted a systematic search to identify papers for this review. Sources: Google Scholar, Scopus, PubMed. Search terms included "microsaccade", "fixational eye movements", AND "eye tracking", and combinations with "detection", "Engbert", "Bayesian", "event camera" "SPAD". We included peer-reviewed articles, conference proceedings, and publicly available benchmarks. Inclusion: empirical studies reporting microsaccade kinematics, neural mechanisms, detection algorithms, or cognitive/clinical applications; review/editorial pieces only when they provided synthesis. Exclusion: studies that did not report primary microsaccade data or were peripheral to fixation (e.g., large saccade-only papers). This procedure was chosen to maximize coverage of influential works while maintaining transparency for replication.

This article presents a C3I-SynMicrosaccade dataset: a synthetic microsaccade dataset designed to enable event-based modelling and classification of microsaccadic eye movements. Using Blender, we generated high-resolution RGB sequences of microsaccades, characterized by small, transient eye rotations around a fixed head pose. Each microsaccade follows a horizontal-boomerang-like trajectory, simulating the natural back-and-forth displacement of the eye during visual fixation. Seven distinct angular classes, ranging from 0.5° to 2.0°, capture varying motion amplitudes while maintaining consistent scene, lighting, and texture conditions. The rendered RGB frames were converted into event-based data streams using the v2e simulator, which replicates the asynchronous behaviour of neuromorphic vision sensors. Temporal durations and event counts were carefully controlled and resampled to ensure class balance and eliminate bias toward motion magnitude. The resulting dataset comprises 175,000 event sequences (87,500 per eye), providing a large-scale, balanced foundation for microsaccade recognition, neuromorphic vision research, and synthetic-to-real transfer learning. This work offers a controlled, reproducible framework for studying fixational eye movements and evaluating event-based algorithms under fine motion dynamics.