Characteristics and generation mechanisms of anticyclonic eddies, cyclonic eddies and dipole eddies in the Mozambique Channel

The discovery of cyclonic and dipole eddies in the Mozambique Channel (MC) indicates that the understanding of the mesoscale eddy characteristics in the MC is incomplete. The distributions of anticyclonic, cyclonic, and dipole eddies along the MC were elucidated in this study using satellite observations. It was observed that these eddies exhibit a preference for emergence and movement in the western MC. The occurrence frequencies of anticyclonic and cyclonic eddies are four and three times per year, respectively, in the narrowest section of the MC. In contrast, the frequency of mesoscale eddies reaches its peak at nine times per year in the central region of the MC. The occurrence of dipole eddies also reaches its peak twice per year in the middle MC. Dipole eddies are more prevalent in the MC and exhibit larger dimensions and shorter lifespans compared to anticyclonic and cyclonic eddies. Mesoscale eddies, which traverse the narrowest section of the MC and propagate southward, are predominantly generated within the western Comoros Basin due to barotropic instability. The southward branch of the Northeast Madagascar Current (NEMC) plays a crucial role in transporting these eddies to the middle MC. The eastern middle MC is also a generation site for mesoscale eddies in addition to the Comoros Basin, where cyclonic eddies are generated twice per year. These cyclonic eddies are also generated due to barotropic instability

Decoding Subject-Driven Cognitive States from EEG Signals for Cognitive Brain–Computer Interface

In this study, we investigated the feasibility of using electroencephalogram (EEG) signals to differentiate between four distinct subject-driven cognitive states: resting state, narrative memory, music, and subtraction tasks. EEG data were collected from seven healthy male participants while performing these cognitive tasks, and the raw EEG signals were transformed into time–frequency maps using continuous wavelet transform. Based on these time–frequency maps, we developed a convolutional neural network model (TF-CNN-CFA) with a channel and frequency attention mechanism to automatically distinguish between these cognitive states. The experimental results demonstrated that the model achieved an average classification accuracy of 76.14% in identifying these four cognitive states, significantly outperforming traditional EEG signal processing methods and other classical image classification algorithms. Furthermore, we investigated the impact of varying lengths of EEG signals on classification performance and found that TF-CNN-CFA demonstrates consistent performance across different window lengths, indicating its strong generalization capability. This study validates the ability of EEG to differentiate higher cognitive states, which could potentially offer a novel BCI paradigm