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Blood Markers May Predict Psychosis in Asian Youths

A new study by researchers from NHG Health's Institute of Mental Health (IMH) and NTU Singapore Lee Kong Chian School of Medicine (LKCMedicine) has

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NZ had third-fastest house price growth in the world for years: Biggest bank warns it probably won’t continue

ANZ's economists have looked at what house prices might do into the longer-term future in their most recent property report.

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LCCI: inflation burdens MSMEs, manufacturers

The Nation Newspaper LCCI: inflation burdens MSMEs, manufacturers

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Pokémon Pocket crosses 200 million downloads

Were you part of the first 200 million Pocket players?

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‘How are we going to survive this?’ Wellington faces six-month wait to halt sewage spill

Millions of litres of sewage have been spilling into the capital city’s waters since February after the catastrophic failure of a Moa Point wastewater plant

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China’s Xi Jinping and Russian President Vladimir Putin meet in Beijing
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China’s Xi Jinping and Russian President Vladimir Putin meet in Beijing

Xi and Putin hold talks just days after US President Donald Trump made an official visit to China.

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Winston Peters wins again – no cuts for MFAT in new Budget

The Ministry of Foreign Affairs and Trade has been excluded from funding cuts, just as Peters said.

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SHAP analysis of an improved EEG-based mental workload classification framework: utilizing data augmentation and explainable AI

Mental workload (MWL) classification using electroencephalogram (EEG) signals is crucial for cognitive neuroscience and is also a challenging research area in brain-computer interface (BCI). Since the EEG signals fluctuate a lot across sessions and individuals, there is a need for a robust classification model that generalizes well for real-world applications. In this work, we used the publicly available dataset “An EEG dataset for cross-session mental workload estimation: passive BCI competition of the Neuroergonomics Conference 2021”, and the standard EEGNet model to classify the MWL into three classes (Low, Med, and High). To improve the performance of the model, a synthetic minority oversampling technique (SMOTE) was used by creating synthetic EEG samples, and key hyperparameters (F1, F2, and D) of EEGNet were systematically varied to identify the optimal configuration. Furthermore, Shapley Additive Explanations (SHAP) analysis was performed to identify the most influential EEG channels for model prediction. The proposed approach achieves the highest accuracy of 80.5% and 82.7% without and with SMOTE, respectively. The comparative analysis showed that applying SMOTE resulted in an average performance improvement of approximately 3%. A Wilcoxon signed-rank test confirmed that this improvement was statistically significant (p < 0.05). Finally, the SHAP analysis revealed that the most informative EEG channels were located over the parieto-occipital and temporal regions, which is consistent with established neurophysiological evidence related to MWL processing. The proposed framework improves both performance and explainability in EEG-based MWL classification, representing a systematic integration of SMOTE and SHAP analysis.

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NASA’s Roman Space Telescope Nears Launch for Epic Hunt Across the Universe

NASA’s Roman Space Telescope could soon begin an epic search for alien worlds, dark matter, and never-before-seen cosmic mysteries.

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Door goes EV with simulated V8 sounds

Mercedes-AMG has replaced the petrol GT 4-Door Coupe with a bespoke electric car offering up to 860kW and fake V8 sounds.

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Rupee tumbles to record low of 96.96 vs USD as US-Iran stalemate stokes global inflation fears

The rupee has reached an all-time low. Elevated oil prices are fueling inflation concerns globally. This situation is leading to increased bets on interest rate hikes. Bond yields are rising, and stock markets are falling. Overseas investors have withdrawn significant funds from India. The currency's depreciation poses challenges for the economy.

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