双阶段运动想象提升脑机接口解码准确率

A motor-imagery brain-computer interface that links imagining a movement with briefly performing it improved the separability of left- and right-limb intentions in an offline EEG study. The researchers recorded data from 97 healthy adults using a new cue-execution paradigm, called CEP-MI, and developed a dual-branch neural network that reached 85.77% accuracy for binary hand classification and 82.47% for four-class classification on the public BCI Competition IV dataset 2a. The results support CEP-MI as an experimental approach and PGCANet as a compact decoding architecture, but they do not yet establish real-time or clinical benefits.

Most motor-imagery experiments present imagery and physical movement in separate trials. That design makes it difficult to observe how brain activity changes continuously as a person moves from an internal representation of movement to actual movement. CEP-MI instead places a three-second countdown above the directional cue, asks participants to imagine the corresponding movement during the countdown, and then has them make a slight limb movement as imagery ends. This sequential arrangement allows the investigators to examine the imagery-to-execution transition while preserving a separate comparison with a conventional arrow-cue motor-imagery task.

The new dataset was collected from 80 men and 17 women aged 18 to 28, with a mean age of 23.6 years; 94 participants were right-handed and three were left-handed. The conventional run contained 48 trials, including 12 for each of four tasks—left hand, right hand, left foot and right foot. The cue-execution run contained 68 trials, with 17 per task. The order of the two runs was counterbalanced. EEG was recorded at 1,000 Hz with 64 electrodes, then offline rereferenced, downsampled to 250 Hz, filtered from 4 to 45 Hz and cleaned of eye-blink components, leaving 62 scalp electrodes for analysis.

Event-related potentials measured at Oz and Pz provided an electrophysiological view of how the added execution stage altered responses to the cues. Repeated-measures analysis found a significant effect of task on P3 amplitude at Pz: the cue-execution condition produced 3.95 ± 0.28 μV, compared with 2.91 ± 0.23 μV in the classic condition. A task-by-limb interaction was also detected for the late positive component during hand trials. For right-hand imagery, however, the LPC difference between cue-execution and classic tasks did not reach statistical significance, and the researchers reported no significant main or interaction effects for N1 or P2. These findings show that the two task structures elicited distinguishable event-related responses, while not uniquely establishing a single cognitive process because ERP components can combine several neural sources.

Time-frequency analysis focused on the 8–30 Hz band at C3, C4 and Cz, electrodes associated with sensorimotor rhythms in the source study. Classic hand imagery produced the expected pattern of contralateral event-related desynchronization, with the strongest reduction around 0.5–1.5 seconds, followed by lateralization between C3 and C4. When execution was added, event-related synchronization and beta rebound became more prominent around 2–3 seconds, especially for hand tasks. Left- and right-foot traces differed most clearly near the end of the imagery period, around 3–4 seconds at C3 and about 1 second at C4. The authors interpret these changes as evidence that motor execution sharpened the neural distinction between movement intentions.

Across the 97 participants, the cue-execution design raised the mean accuracy of every tested classifier for hand imagery. Common Space Pattern improved from 54.06% to 60.34%, Filter Bank CSP from 57.05% to 61.34%, EEGNet from 70.72% to 75.05%, and EEG Conformer from 77.73% to 80.41%. PGCANet rose from 80.82 ± 11.37% to 85.77 ± 6.49%, a 4.95-percentage-point increase that was reported as statistically significant in paired participant comparisons. The improvement was smaller for foot imagery: PGCANet increased from 80.00 ± 8.21% to 82.06 ± 8.26%, while only its foot-task gain reached nominal statistical significance among the compared models.

PGCANet was designed to combine two complementary views of the EEG rather than compress the full multichannel signal into one undifferentiated representation. Its first branch learns spatial and temporal features from all retained channels. A channel-aware module assigns trainable weights to individual electrodes, allowing the network to emphasize informative signals and suppress noisy ones. This is followed by temporal and spatial convolutions and multi-head self-attention, which models relationships across time and channels. The design uses the attention component rather than an entire Transformer to limit computational overhead and the risk of overfitting on relatively small motor-imagery datasets.

The second branch uses prior knowledge about lateralized sensorimotor activity to focus on C3 and C4. Signals from those electrodes are converted into time-frequency maps with a continuous wavelet transform using a Morlet mother wavelet, selected for its balance between time and frequency resolution. A shallow convolutional network extracts patterns at two spatial scales, followed by average pooling, batch normalization and dropout. Rather than flattening and concatenating both branches directly, PGCANet combines them at the decision level. Its consistency-constrained loss applies two independent dropout passes to each branch and uses Kullback–Leibler divergence to keep their prediction distributions similar while retaining the information contributed by each branch.

On BCI Competition IV dataset 2a, PGCANet achieved an average four-class accuracy of 82.47% across nine participants, compared with 81.23% for Flashlight-Net, 78.66% for EEG Conformer, 76.20% for FBCNet and 74.50% for EEGNet. The mean performance nevertheless varied substantially among individuals, with reported values ranging from 63.79% to 93.97%. Ablation tests on the same dataset indicated that removing any of the three main components reduced performance. The full model scored 82.47%, compared with 80.20% without the channel-aware module, 80.65% without the time-frequency branch and 80.86% without the consistency-constrained loss, making the fusion constraint the largest contributor in those tests.

The authors trained the network for 2,000 epochs with the Adam optimizer, an initial learning rate of 2 × 10⁻⁴, a warm-up period, batch size of 32, dropout of 0.5 and label smoothing of 0.1. The consistency weight was selected empirically as λ = 0.02. The resulting model contains 0.73 million parameters, occupies about 2.9 MB in 32-bit floating-point format and requires approximately 128.2 million floating-point operations for one forward inference. A t-SNE visualization showed clearer separation among class representations after processing, but the authors cautioned that two-dimensional projections can exaggerate subject-specific clusters, making the quantitative classification results the stronger evidence.

The findings remain limited by their offline design and the use of healthy young adults. The analysis covered only hand and foot imagery, and the decoding results were not tested in a real-time BCI, among people with neurological or musculoskeletal disorders, or during fine finger control. The countdown and added movement also made the cue-execution task more complex, potentially dividing attention as well as shifting neural resources toward motor preparation. Consequently, the study shows improved discrimination and a useful experimental bridge between imagery and execution, but further online and clinical validation is required before claims about neurorehabilitation, motor assistance or practical performance can be made.

Subject of Research: A dual-stage motor-imagery EEG paradigm and neural network for decoding left- and right-limb movement intentions.

Article Title: CEP-MI: a novel cue-execution dual-stage motor imagery paradigm with PGCANet decoding architecture

Article References: Li, J., Yu, P., Qiao, H., Zheng, Z., Wang, C., Weng, Z., & Gong, C. (2026). CEP-MI: a novel cue-execution dual-stage motor imagery paradigm with PGCANet decoding architecture. Frontiers in Human Neuroscience, 20, 1908608. https://doi.org/10.3389/fnhum.2026.1908608

Image Credits: AI Generated

DOI: 10.3389/fnhum.2026.1908608

Keywords: brain-computer interfaces, motor imagery, electroencephalography, neural decoding, event-related potentials, sensorimotor rhythms, deep learning, neurorehabilitation, CEP-MI, novel, cue-execution, dual-stage