Despite advancements in language-controlled reinforcement learning (LC-RL) for basic domains and straightforward commands (e.g., object manipulation and navigation), effectively extending LC-RL to comprehend and execute high-level or abstract instructions in complex, multi-agent environments, such as football games, remains a significant challenge. To address this gap, we introduce Language-Controlled Diverse Style Policies (LCDSP), a novel LC-RL paradigm specifically designed for complex scenarios. LCDSP comprises two key components: a Diverse Style Training (DST) method and a Style Interpreter (SI). The DST method efficiently trains a single policy capable of exhibiting a wide range of diverse behaviors by modulating agent actions through style parameters (SP). The SI is designed to accurately and rapidly translate high-level language instructions into these corresponding SP. Through extensive experiments in a complex 5v5 football environment, we demonstrate that LCDSP effectively comprehends abstract tactical instructions and accurately executes the desired diverse behavioral styles, showcasing its potential for complex, real-world applications.

Employing a minimal array of electroencephalography (EEG) channels for neonatal sleep stage classification is essential for data acquisition in the Internet of Medical Things (IoMT), as single-channel and edge-based features can reduce data transfer and processing requirements, enhancing cost-effectiveness and practicality. In this paper, we evaluate the efficacy of a single channel and the viability of a binary classification scheme for discerning awake and sleep states and transitions to quiet sleep. For this, two datasets of EEG signals for neonate sleep analysis were recorded from Children's Hospital of Fudan University, Shanghai, comprising recordings from 64 and 19 neonates, respectively. From each epoch, a diverse ensemble of 490 features was extracted through a blend of discrete and continuous wavelet transforms (DWT, CWT), spectral statistics, and temporal features. In addition, we introduced an innovative hybrid univariate and ensemble feature selection approach with multidomain feature fusion, a stacking-based ensemble classifier that outperforms existing work. We achieved 90.37%, 91.13%, and 94.88% accuracy for sleep/awake, quiet sleep/non-quiet sleep, and quiet sleep/awake, respectively. This was corroborated by significant Kappa values of 77.5%, 80.29%, and 89.76%. Using SelectPercentile, we devised three distinct feature selection mechanisms: one using DWT, one with CWT, and another incorporating both spectral and temporal features. Subsequently, SelectKBest was used to determine the most effective features. For our stacked model, we incorporated a trifecta of the ExtraTree model with variable estimators, a Random Forest, and an Artificial Neural Network (ANN) as base classifiers, and for the final prediction phase, ANN was implemented again. The model's performance was evaluated using K-fold and leave-one-subject cross-validation.

Acromegaly, a chronic endocrine disorder, requires early diagnosis to prevent severe complications. Existing deep learning-based diagnostic methods often prioritize accuracy at the expense of computational complexity and feature diversity, limiting their deployment in resource-constrained IoT environments. This paper proposes Mobile Hybrid Mamba (MHMamba), a lightweight model for edge-based acromegaly screening that synergistically integrates Inverted Residual Attention Convolution (IRAC) and VMamba. The design leverages the hierarchical local feature extraction of convolutional networks in IRAC to effectively capture fine-grained pathological patterns, while incorporating VMamba’s selective state-space mechanism for linear-complexity global modeling of anatomical dependencies. This complementary fusion enables comprehensive representation learning of both localized manifestations and structural correlations in medical images. Consequently, MHMamba achieves state-of-the-art performance (2.8–3.1% improvement in precision, recall, and F1-score) while maintaining ultra-lightweight parameters (13.882M) and low computational overhead (2.054G FLOPs). Crucially, MHMamba’s mobile-friendly architecture enables real-time facial analysis on smartphones, making it suitable for IoT-driven telemedicine and decentralized health monitoring. Through occlusion experiments and GradCAM++ visualization, we identify key facial regions (nose, mouth, and cheekbones) critical for diagnosis, aligning with clinical biomarkers. The model’s interpretability and portability position it as a pivotal tool for IoT-enabled smart healthcare systems, bridging the gap between AI-driven diagnostics and edge device deployment.

A timely diagnosis and treatment of sleep disorders in neonates during their first week of life is crucial. Current methods for staging neonatal sleep rely heavily on multiple electroencephalography (EEG) channels. These channels increase computational complexity, require a large amount of data to be transferred to the cloud, and may cause skin irritation. We propose an innovative automated classification approach that integrates multi-view feature fusion, AdaptiSelect-based feature optimization, the smart cloud data transfer and reconstruction (STREAM) module, and a rotational ensemble stacking model. The data reduction module significantly enhances edge-cloud systems’ performance in IoT-based healthcare environments by reducing data transmission by a factor of 153.6 through efficient feature selection and compact data packet formation. This module ensures minimal bandwidth usage, reduces the computational load on resource-constrained edge devices, and lowers cloud storage requirements while maintaining full data reconstruction. The dataset used in this research combines two large datasets collected over four years from the Children’s Hospital Fudan University, Shanghai. A unique set of 315 features are extracted from each epoch of a single channel using flexible analytical wavelet transform (FAWT), dual-tree complex wavelet transform (DTCWT), enhanced covariance (ECOV), and spectral features based on α, β, θ, and δ brain waves. These features are refined using AdaptiSelect, achieving an accuracy of 81.16% and a Kappa of 72.17% with one channel. Accuracy improves to 82.79% with a Kappa of 74.70% when using two channels, validated through 10-fold cross-validation. Additionally, Leave-One-Subject-Out crossvalidation (LOSO-CV) further demonstrates the effectiveness of the proposed approach as a generalized solution. Using both single and multichannel setups, the proposed approach outperforms the most significant state-of-the-art methods in neonatal sleep analysis.

Rapid eye movement (REM) sleep without atonia (RSWA) is a critical diagnostic criterion for REM sleep behavior disorder (RBD). Current clinical practices rely on time-consuming manual annotation, increasing workload, and introducing variability. Existing automated methods, including CNN, LSTM, Transformer, and their combinations, fail to fully exploit the inherent physiological relationship between sleep staging and RSWA detection, while also facing challenges such as severe data imbalance and the complex fusion of multichannel signals. To address these limitations, we propose a multi-task learning framework that jointly optimizes both tasks. Our Multi-scale Information Attention Bottleneck Network (MIABNet) backbone improves multichannel fusion. Building upon MIABNet, the Multi-task RBD Network (MtRBD) implements dynamic feature enhancement, facilitating cross-task information flow while preserving physiological relationships. On the clinical CZ-RBD dataset collected from 30 patients over 59 nights at Shanghai Changzheng Hospital, our framework achieved the accuracy of 83.0% for sleep staging and 93.6% for RSWA detection, with an accuracy of 77.6% for critical RSWA event identification, outperforming single-task methods in cross-subject validation. Through modality selection and Grad-CAM visualization, we enhance clinical interpretability, providing reliable support for early detection of RBD and related neurodegenerative diseases.

Recently, Electrooculography-based Human-Computer Interaction (EOG-HCI) technology has gained widespread attention in industrial areas, including assistive robots, augmented reality in gaming, etc. However, as the fundamental step of EOG-HCI, accurate eye movement classification (EMC) still faces a significant challenge, where their constraints in extracting discriminative features limit the performance of most existing works. To address this issue, a Residual Self-Calibrated Network with Multi-Scale Channel Attention (RSCA), focusing on efficient feature extraction and enhancement is proposed. The RSCA network first employs three self-calibrated convolution blocks within a hierarchical residual framework to fully extract the discriminative multi-scale features. Then, a multi-scale channel attention module adaptively weights the learned features to screen out the discriminative representation by aggregating the multi-scale context information along the channel dimension, thus further boosting the performance. Comprehensive experiments were performed using 5 public datasets and 7 prevailing methods for comparative validation. The results confirm that the RSCA network outperforms all other methods significantly, establishing a state-of-the-art benchmark for EOG-based EMC. Furthermore, thorough ablation analyses confirm the effectiveness of the employed modules within the RSCA network, providing valuable insights for the design of EOG-based deep models.

Stroke is the second leading cause of disability and mortality worldwide. Extensive researches on rehabilitation assessment for stroke survivors using wearable plantar pressure sensors often suffer from troublesome processes and unsatisfactory results due to excessive sensors and sparse pressure sensing structures. To address this, we developed an automatic assessment system for the recovery stage of stroke patients based solely on smart high-density flexible wearable plantar pressure insoles and an ensemble learning classification algorithm (ELCA). This system quantitatively evaluates the stroke patient's recovery progress. The data from 37 stroke patients were collected using self-developed smart insoles, from which multidimensional high-order features were extracted. ELCA employing seven basic classifiers was then developed to quantitatively assess recovery stages. Finally, the leave-one-out method was used to evaluate the performance of classifiers. The results show the ELCA has the best performance, achieving an accuracy, specificity, and sensitivity of up to 91.890/0, 97.30%, and 91.89% respectively, preliminarily demonstrating the system possesses excellent capabilities in quantitatively assessing rehabilitation progress. This solution is expected to provide an objective and practical method for long-term home rehabilitation monitoring of stroke patients.

Sleep is essential for health, with poor-quality sleep may link to cognitive impairment, increased disease risk, etc. Traditional sleep monitoring systems, such as polysomnography(PSG), are comprehensive but costly, uncomfortable, and confined to clinical settings. Recent wearable sleep monitoring devices typically employ single-channel EEG signal from the forehead, which can only acquire single-modal signals and may limit the accuracy of sleep analysis to some extent. To address these issues, this paper proposes a low-cost, multi-modal, wearable and comfortable headband system for sleep monitoring. The headband integrates six EEG sensors, a six-axis inertial measurement unit (IMU) and a temperature sensor to collect EEG signals, motion data, and environmental temperature, and can also derive information from other modalities such as heart beat, respiration rate, body movement, etc. The headband system integrates multiple sensors, with the EEG sensors primarily using flexible claw-shaped dry electrodes that penetrate the hair to make sufficient contact with the scalp for precise detection of minute signals. This system provides a wealth of multi-modal information, which offers a solution for precise sleep analysis and the identification of sleep disorders such as insomnia and sleep apnea. To verify the performance of the headband prototype, comprehensive experiments were performaed as compare to PSG. Experimental results show that the Pearson correlation coefficient of EEG signals can reach 0.9435, and the RR interval derived from IMU is only 0.001 seconds different from that obtained from PSG ECG signals, proving a high consistent with the standard results measured by PSG.

Surface electromyogram (sEMG)-based multidegree of freedom (DoF) finger force estimation for the prosthesis and armband applications has obtained increasing attention in the human–machine interface (HMI) field. However, few studies have explored the relation between force estimation performance and coverage area of sEMG electrodes. To address the needs of transradial amputees with varying stump lengths, we investigated the force estimation performance using 16 different electrode layouts covering different forearm areas. Additionally, since the position of the armband affects force estimation performance, we evaluated how model performance varies with the armband worn from the wrist to the elbow. This allows users to select their armband position based on a tradeoff between model performance and practical convenience. We acquired 256-channel forearm sEMG and multi-DoF finger force data from 20 intact subjects. Each subject participated in the experiment on two different days (3 to 25 days apart). Benchmark features were extracted and least squares-based linear finite impulse response models were constructed to estimate the multi-DoF finger force. Both intra-day and interday results were reported for comparison. As a result, the interday regression root mean square error ranged from 8.71±0.80% to 10.98±0.98% of maximum force for prosthesis application and from 9.45±0.79% to 10.82±0.90% of maximum force for armband application. In summary, this work enables users to customize their systems based on their physical conditions and requirements.

Automatic sleep staging offers a quick and objective assessment for quantitatively interpreting sleep stages in neonates. However, most of the existing studies either do not encompass any temporal information, or simply apply neural networks to exploit temporal information at the expense of high computational overhead and modeling ambiguity. This limits the application of these methods to multiple scenarios. In this paper, a sequential end-to-end sleep staging model, SeqEESleepNet, which is competent for parallelly processing sequential epochs and has a fast training rate to adapt to different scenarios, is proposed. SeqEESleepNet consists of a sequence epoch generation (SEG) module, a sequential multi-scale convolution neural network (SMSCNN) and squeeze and excitation (SE) blocks. The SEG module expands independent epochs into sequential signals, enabling the model to learn the temporal information between sleep stages. SMSCNN is a multi-scale convolution neural network that can extract both multi-scale features and temporal information from the signal. Subsequently, the followed SE block can reassign the weights of features through mapping and pooling. Experimental results exhibit that in a clinical dataset, the proposed method outperforms the state-of-the-art approaches, achieving an overall accuracy, F1-score, and Kappa coefficient of 71.8%, 71.8%, and 0.684 on a three-class classification task with a single channel EEG signal. Based on our overall results, we believe the proposed method could pave the way for convenient multi-scenario neonatal sleep staging methods.

Gaze estimation based on electrooculograms (EOGs) has been widely explored. However, the inter-subject variability of EOGs still leaves a significant challenge for practical applications. It contributes to performance degradation when handling inter-subject issues. In this paper, an unsupervised transfer learning approach with an adaptive reweighting and resampling (ARR) strategy to fully consider individual variability is proposed for EOG-based gaze angle estimation. It allows quantifying domain shifts by leveraging the source-target similarities, reweighting and resampling the source data to retain relevant instances and disregard irrelevant instances during adaptation. Specifically, our proposed methodology first assesses the domain shifts via decomposing transformation matrices, which are estimated between the training subjects (denoted as multi-source domains) and the test subject (denoted as target domain). Then, the multi-domain shifts are assigned as weighted indicators to resample the multi-source domains for model training. Comparative experiments with several prevailing transfer learning methods including CORrelation ALignment (CORAL), Geodesic Flow Kernel (GFK), Joint Distribution Adaptation (JDA), Transfer component analysis (TCA), and Balanced distribution adaption (BDA) using two different normalization processes were conducted on a realistic scenario across 18 subjects. Experimental results demonstrate that the ARR strategy can significantly improve performance (mean absolute error (MAE) reduction: 7.0%, root mean square error (RMSE) reduction: 6.3%), outperforming the prevailing methods. Besides, the impacts of data diversity and data size on ARR strategy are further investigated. It exhibits that data size is more important than data diversity for EOG-based gaze angle estimation, and also presents the benefits of the ARR strategy for dealing with practical scenarios.

Sleep staging plays a critical role in evaluating the quality of sleep. Currently, most studies are either suffering from dramatic performance drops when coping with varying input modalities or unable to handle heterogeneous signals. To handle heterogeneous signals and guarantee favorable sleep staging performance when a single modality is available, a pseudo-siamese neural network (PSN) to incorporate electroencephalography (EEG), electrooculography (EOG) characteristics is proposed (PSEENet). PSEENet consists of two parts, spatial mapping modules (SMMs) and a weight-shared classifier. SMMs are used to extract high-dimensional features. Meanwhile, joint linkages among multi-modalities are provided by quantifying the similarity of features. Finally, with the cooperation of heterogeneous characteristics, associations within various sleep stages can be established by the classifier. The evaluation of the model is validated on two public datasets, namely, Montreal Archive of Sleep Studies (MASS) and SleepEDFX, and one clinical dataset from Huashan Hospital of Fudan University (HSFU). Experimental results show that the model can handle heterogeneous signals, provide superior results under multimodal signals and show good performance with single modality. PSEENet obtains accuracy of 79.1%, 82.1% with EEG, EEG and EOG on Sleep-EDFX, and significantly improves the accuracy with EOG from 73.7% to 76% by introducing similarity information.

Despite the recent advances in automatic sleep staging, few studies have focused on real-time sleep staging to promote the regulation of sleep or the intervention of sleep disorders. In this paper, a novel network named SwSleepNet, that can handle both precisely offline sleep staging, and online sleep stages prediction and calibration is proposed. For offline analysis, the proposed network coordinates sequence broadening module (SBM), sequential CNN (SCNN), squeeze and excitation (SE) block, and sequence consolidation module (SCM) to balance the operational efficiency of the network and the comprehensive feature extraction. For online analysis, only SCNN and SE are involved in predicting the sleep stage within a short-time segment of the recordings. Once more than two successive segments have disparate predictions, the calibration mechanism will be triggered, and contextual information will be involved. In addition, to investigate the appropriate time of the segment that is suitable to predict a sleep stage, segments with five-second, three-second, and two-second data are analyzed. The performance of SwSleepNet is validated on two publicly available datasets Sleep-EDF Expanded and Montreal Archive of Sleep Studies (MASS), and one clinical dataset Huashan Hospital Fudan University (HSFU), with the offline accuracy of 84.5%, 86.7%, and 81.8%, respectively, which outperforms the state-of-the-art methods. Additionally, for the online sleep staging, the dedicated calibration mechanism allows SwSleepNet to achieve high accuracy over 80% on three datasets with the short-time segments, demonstrating the robustness and stability of SwSleepNet. This study presents a real-time sleep staging architecture, which is expected to pave the way for accurate sleep regulation and intervention.

Due to the limitations of large size, non-random movement, high cost and complex equipment, the video recognition and pressure testing platform used for gait monitoring can only be operated in laboratory environment. Some insole devices invented in recent years are basically equipped with other modules, making them inconvenient to wear. To solve the above problems, this paper proposes intelligent flexible pressure insoles with low cost and high resolution, as well as convenience and comfortness. The insole collects the plantar pressure distribution and motion data of daily walking through 12 x 4 flexible pressure array and accelerometer sensor, and then wirelessly transmits the data to the upper computer through Bluetooth. Material property tests were conducted and the results showed robustness and stability of the pressure sensor. The feasibility of the insole for gait monitoring is verified through preliminary walking experiments. The results show that of the proposed insole is characterized by low cost, high resolution, portability and comfortableness. Additionally, it can be used directly into the shoe without any external module, regardless of site and environment restrictions. The insole can meet the needs of daily gait monitoring and provide more quantitative reference data for gait rehabilitation.

Tedious parameter settings and poor performances seriously affect the entropy estimation's effectiveness in time series analysis. To solve these limits, we propose a conceptually novel definition, cumulative diversity pattern entropy (CDEn), focusing on eliminating parameter selections and improving quantization accuracy, stability, and robustness. The CDEn algorithm consists of three steps: 1) improved phase-space reconstruction (IPSR) with constant embedding dimension$m= 2$and time delay$\tau =1$; 2) diversity pattern partition generated by the cosine similarity between adjacent vectors; and 3) entropy calculation based on the normalized cumulative probability distribution. Numerical experiments are performed using 7 synthetic datasets and 15 baseline entropy methods for comparative validation. The results confirm CDEn's best description of chaotic/stochastic dynamics with the highest quantization accuracy and the lowest error rate of 2.04%. The coefficient of variation (CV) results also verify CDEn's excellent quantization stability with CV lower than 10−2. The relative change rate results demonstrate that CDEn achieves the best robustness to data length and noise. Finally, the entropy algorithms are applied to a real-world dataset, i.e., neonatal sleep EEG analysis. The results further confirm that suggested CDEn outperforms the state-of-the-art entropy methods, with the minimum outliers and best statistical significance (highest mean of effect size, 1.22) in characterizing the neurodynamics of different sleep stages.

Deep learning methods have become an important tool for automatic sleep staging in recent years. However, most of the existing deep learning-based approaches are sharply constrained by the input modalities, where any insertion, substitution, and deletion of input modalities would directly lead to the unusable of the model or a deterioration in the performance. To solve the modality heterogeneity problems, a novel network architecture named MaskSleepNet is proposed. It consists of a masking module, a multi-scale convolutional neural network (MSCNN), a squeezing and excitation (SE) block, and a multi-headed attention (MHA) module. The masking module consists of a modality adaptation paradigm that can cooperate with modality discrepancy. The MSCNN extracts features from multiple scales and specially designs the size of the feature concatenation layer to prevent invalid or redundant features from zero-setting channels. The SE block further optimizes the weights of the features to optimize the network learning efficiency. The MHA module outputs the prediction results by learning the temporal information between the sleeping features. The performance of the proposed model was validated on two publicly available datasets, Sleep-EDF Expanded (Sleep-EDFX) and Montreal Archive of Sleep Studies (MASS), and a clinical dataset, Huashan Hospital Fudan University (HSFU). The proposed MaskSleepNet can achieve favorable performance with input modality discrepancy, e.g. for single-channel EEG signal, it can reach 83.8%, 83.4%, 80.5%, for two-channel EEG+EOG signals it can reach 85.0%, 84.9%, 81.9% and for three-channel EEG+EOG+EMG signals, it can reach 85.7%, 87.5%, 81.1% on Sleep-EDFX, MASS, and HSFU, respectively. In contrast the accuracy of the state-of-the-art approach which fluctuated widely between 69.0% and 89.4%. The experimental results exhibit that the proposed model can maintain superior performance and robustness in handling input modality discrepancy issues.

The clinical manifestations of OSA (obstructive sleep apnea) are night sleep snoring accompanied by apnea. fNIRS (functional near-infrared spectroscopy) can measure the relative concentration changes of oxyhemoglobin and deoxyhemoglobin, thereby measuring hemodynamics and oxygenation. Combined with the clinical manifestations of obstructive sleep apnea, fNIRS technology can be used in the monitoring of OSA. According to the principle of fNIRS, a miniaturized, wireless, and flexible device is designed in the article. The device can measure the change of blood oxygen concentration in the forehead of the brain, so that achieve the purpose of detecting OSA. To verify the effectiveness of the equipment, a breathing experiment was designed in the article to compare the equipment with the existing NIRX system. To our best knowledge, this is the first work that proposed a miniaturized flexible sleep apnea system based on monitoring fluctuations of cerebral hemodynamics. Meanwhile, the feasibility of the proposed system was verified. However, the stability of the equipment and related data processing algorithms need to be enhanced. The proposed system is expected to pave the way for the investigation of the involvement of the cerebral hemodynamics in the pathogenesis of OSA patients.

Sleep posture, which affects the quality of sleep and could lead to medical conditions, such as pressure ulcers, is a key metric for sleep analysis in Internet of Medical Things (IoMT). In this article, a real-time and low-cost smart mat system for sleep posture recognition based on frequency channel selection is proposed. The system can recognize postures unobtrusively with a dense flexible sensor array. In addition, to enable real-time recognition with a relatively low-cost STM32 processor system, a lightweight algorithm that includes frequency channel selection, model pretraining, and real-time classification is proposed. Through a series of short-term and overnight experiments with 21 subjects, the feasibility and reliability of the proposed system were evaluated. Experimental results show that the accuracy of the short-term experiment is up to 95.43% and of the overnight experiment is up to 86.80% for four posture categories (supine, prone, right, and left) classification. The model size is just 56 kB which is much smaller than other methods. The runtime of the complete algorithm is about 6 ms with a low-power STM32 embedded system, which shows the system’s ability to provide real-time posture recognition. As an edge device, the proposed system could lead to the development of fast, convenient, and low-cost sleep posture recognition products for IoMT.

Sleep posture, as a crucial index for sleep quality assessment and pressure ulcer prevention, has been widely studied for medical diagnoses and sleep disease treatment. In this paper, an unobtrusive smart mat system for sleep posture recognition is proposed, which is based on a dense flexible sensor array and printed electrodes and along with an algorithmic framework. With the dense flexible sensor array, the system offers a comfortable and high-resolution solution for long-term pressure sensing. Meanwhile, compared with other large-area and low-density mat systems, it reduces the area to minimize manufacturing cost and computational complexity, while also increases the density of the sensor to improve accuracy. To distinguish the sleep postures, the algorithmic framework that includes pre-processing, feature extraction, and posture classification is developed. Pilot studies in two scenarios including subject-dependent and subject- independent classification are performed with 7 persons for 4 different postures recognition. The experimental results show that the accuracy of the smart mat system can achieve over 78% using Support Vector Machines (SVMs) and k-Nearest Neighbor (kNN) for the subject-independent scenario. For the subject-dependent scenario, the accuracy can reach over 95%. It proves that the proposed method can recognize different sleep postures effectively.

Automatic sleep staging methods usually extract hand-crafted features or network trained features from signals recorded by polysomnography (PSG), and then estimate the stages by various classifiers. In this study, we propose a classification approach based on a hierarchical neural network to process multi-channel PSG signals for improving the performance of automatic five-class sleep staging. The proposed hierarchical network contains two stages: comprehensive feature learning stage and sequence learning stage. The first stage is used to obtain the feature matrix by fusing the hand-crafted features and network trained features. A multi-flow recurrent neural network (RNN) as the second stage is utilized to fully learn temporal information between sleep epochs and fine-tune the parameters in the first stage. The proposed model was evaluated by 147 full night recordings in a public sleep database, the Montreal Archive of Sleep Studies (MASS). The proposed approach can achieve the overall accuracy of 0.878, and the F1-score is 0.818. The results show that the approach can achieve better performance compared to the state-of-the-art methods. Ablation experiment and model analysis proved the effectiveness of different components of the proposed model. The proposed approach allows automatic sleep stage classification by multi-channel PSG signals with different criteria standards, signal characteristics, and epoch divisions, and it has the potential to exploit sleep information comprehensively.

Steady-state visually evoked potentials (SSVEP) can be elicited by a large variety of stimuli. To the best of our knowledge, the size and shape effect of stimuli has never been investigated in the literature. We study the relationship between the visual parameters (size and shape) of the stimulation and the resulting brain response. A tentative physiological interpretation is proposed and the potential of the effect in a BrainComputer Interface is outlined.