Objective: To determine the extent and implications of internal human electroencephalography (EEG) research conducted by the tobacco industry.
Methods: This study analysed internal documents that describe the results of human EEG studies conducted by tobacco manufacturers. Emphasis was placed on documents that pertain to the application of EEG to product evaluation efforts.
Results: Internal EEG research was used to determine dose-response relations and effective threshold levels for nicotine, emphasising the importance of form and mechanism of nicotine delivery for initiating robust central nervous system (CNS) effects. Internal studies also highlight the importance of human behaviour during naturalistic smoking, revealing neurophysiological markers of compensation during smoking of reduced nicotine cigarettes. Finally, internal research demonstrates the effectiveness of EEG for the evaluation of non-nicotine phenomena including smoke-component discrimination by smokers, classification of sensory characteristics and measurement of hedonics and other subjective effects.
Conclusions: Tobacco manufacturers successfully developed objective, EEG-based techniques to evaluate the influence of product characteristics on acceptance and use. Internal results suggest that complex interactions between pharmacological, sensory and behavioural factors mediate the brain changes that occur with smoking. These findings have implications for current proposals regarding the regulation of tobacco products and argue for the incorporation of objective measures of product effects when evaluating the health risks of new and existing tobacco products.
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http://dx.doi.org/10.1136/tc.2009.032805 | DOI Listing |
J Integr Neurosci
January 2025
Department of Psychology, The Affiliated Hospital of Jiangnan University, 214151 Wuxi, Jiangsu, China.
Background: Deficits in emotion recognition have been shown to be closely related to social-cognitive functioning in schizophrenic. This study aimed to investigate the event-related potential (ERP) characteristics of social perception in schizophrenia patients and to explore the neural mechanisms underlying these abnormal cognitive processes related to social perception.
Methods: Participants included 33 schizophrenia patients and 35 healthy controls (HCs).
Sensors (Basel)
January 2025
Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK.
A generative adversarial network (GAN) makes it possible to map a data sample from one domain to another one. It has extensively been employed in image-to-image and text-to image translation. We propose an EEG-to-EEG translation model to map the scalp-mounted EEG (scEEG) sensor signals to intracranial EEG (iEEG) sensor signals recorded by foramen ovale sensors inserted into the brain.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Department of Mechanical Engineering, College of Engineering, Imam Mohammad Ibn Saud Islamic University, Riyadh 11432, Saudi Arabia.
Enhancing motor disability assessment and its imagery classification is a significant concern in contemporary medical practice, necessitating reliable solutions to improve patient outcomes. One promising avenue is the use of brain-computer interfaces (BCIs), which establish a direct communication pathway between users and machines. This technology holds the potential to revolutionize human-machine interaction, especially for individuals diagnosed with motor disabilities.
View Article and Find Full Text PDFSensors (Basel)
January 2025
University of Zagreb Faculty of Electrical Engineering and Computing, Unska 3, 10000 Zagreb, Croatia.
Major depressive disorder (MDD) is associated with substantial morbidity and mortality, yet its diagnosis and treatment rates remain low due to its diverse and often overlapping clinical manifestations. In this context, electroencephalography (EEG) has gained attention as a potential objective tool for diagnosing depression. This study aimed to evaluate the effectiveness of EEG in identifying MDD by analyzing 140 EEG recordings from patients diagnosed with depression and healthy volunteers.
View Article and Find Full Text PDFSensors (Basel)
January 2025
School of Automation, Beijing Institute of Technology, Beijing 100081, China.
Existing autonomous driving systems face challenges in accurately capturing drivers' cognitive states, often resulting in decisions misaligned with drivers' intentions. To address this limitation, this study introduces a pioneering human-centric spatial cognition detecting system based on drivers' electroencephalogram (EEG) signals. Unlike conventional EEG-based systems that focus on intention recognition or hazard perception, the proposed system can further extract drivers' spatial cognition across two dimensions: relative distance and relative orientation.
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