AIMC Topic: Brain

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As above, so below? Towards understanding inverse models in BCI.

Journal of neural engineering
OBJECTIVE: In brain-computer interfaces (BCI), measurements of the user's brain activity are classified into commands for the computer. With EEG-based BCIs, the origins of the classified phenomena are often considered to be spatially localized in the...

ABroAD: A Machine Learning Based Approach to Detect Broadband NIRS Artefacts.

Advances in experimental medicine and biology
Artefacts are a common and unwanted aspect of any measurement process, especially in a clinical environment, with multiple causes such as environmental changes or motion. In near-infrared spectroscopy (NIRS), there are several existing methods that c...

Discrimination of Complex Activation Patterns in Near Infrared Optical Tomography with Artificial Neural Networks.

Advances in experimental medicine and biology
Near-infrared optical tomography (NIROT) has great promise for many clinical problems. Here we focus on the study of brain function. During NIROT image reconstruction of brain activity, an inverse problem has to be solved that is sensitive to small s...

Speech emotion recognition based on brain and mind emotional learning model.

Journal of integrative neuroscience
Speech emotion recognition is a challenging obstacle to enabling communication between humans and machines. The present study introduces a new model of speech emotion recognition based on the relationship between the human brain and mind. According t...

Multi-task fused sparse learning for mild cognitive impairment identification.

Technology and health care : official journal of the European Society for Engineering and Medicine
BACKGROUND: Brain functional connectivity network (BFCN) has been widely applied to identify biomarkers for the brain function understanding and brain diseases analysis.

Relative wave energy-based adaptive neuro-fuzzy inference system for estimation of the depth of anaesthesia.

Journal of integrative neuroscience
The advancement in medical research and intelligent modeling techniques has lead to the developments in anaesthesia management. The present study is targeted to estimate the depth of anaesthesia using cognitive signal processing and intelligent model...

Embedding Anatomical or Functional Knowledge in Whole-Brain Multiple Kernel Learning Models.

Neuroinformatics
Pattern recognition models have been increasingly applied to neuroimaging data over the last two decades. These applications have ranged from cognitive neuroscience to clinical problems. A common limitation of these approaches is that they do not inc...