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		<title>How a Hidden Flaw in EEG Machine Learning Inflates Accuracy by 35 Percentage Points</title>
		<link>https://bioengineer.org/how-a-hidden-flaw-in-eeg-machine-learning-inflates-accuracy-by-35-percentage-points/</link>
		
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		<pubDate>Sat, 17 Oct 2026 16:44:56 +0000</pubDate>
				<category><![CDATA[Health]]></category>
		<category><![CDATA[ADHD]]></category>
		<category><![CDATA[balanced accuracy]]></category>
		<category><![CDATA[clinical neuroscience]]></category>
		<category><![CDATA[cross-validation]]></category>
		<category><![CDATA[data leakage]]></category>
		<category><![CDATA[deep learning]]></category>
		<category><![CDATA[EEG]]></category>
		<category><![CDATA[Machine Learning]]></category>
		<category><![CDATA[Parkinson’s disease]]></category>
		<category><![CDATA[Reproducibility]]></category>
		<category><![CDATA[subject-independent validation]]></category>
		<category><![CDATA[window leakage]]></category>
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					<description><![CDATA[A new study shows that randomly splitting EEG windows between training and test sets inflates diagnostic accuracy by roughly 35 percentage points across ADHD and Parkinson's cohorts, and that only participant-disjoint evaluation reveals the true, far more modest performance of clinical brain-signal classifiers.]]></description>
		
		
		
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