Dimitrios A. Adamos

Performance evaluation of PCA-based spike sorting algorithms

Deciphering the electrical activity of individual neurons from multi-unit noisy recordings is critical for understanding complex neural systems. A widely used spike sorting algorithm is being evaluated for single-electrode nerve trunk recordings. The algorithm is based on principal component analysis (PCA) for spike feature extraction. In the neuroscience literature it is generally assumed that the use of the first two or most commonly three principal components is sufficient. We estimate the optimum PCA-based feature space by evaluating the algorithms performance on simulated series of action potentials. A number of modifications are made to the open source nev2lkit software to enable systematic investigation of the parameter space. We introduce a new metric to define clustering error considering over-clustering more favorable than under-clustering as proposed by experimentalists for our data. Both the program patch and the metric are available online. Correlated and white Gaussian noise processes are superimposed to account for biological and artificial jitter in the recordings. We report that the employment of more than three principal components is in general beneficial for all noise cases considered. Finally, we apply our results to experimental data and verify that the sorting process with four principal components is in agreement with a panel of electrophysiology experts.

NASS: An empirical approach to spike sorting with overlap resolution based on a hybrid noise-assisted methodology

Background noise and spike overlap pose problems in contemporary spike-sorting strategies. We attempted to resolve both issues by introducing a hybrid scheme that combines the robust representation of spike waveforms to facilitate the reliable identification of contributing neurons with efficient data learning to enable the precise decomposition of coactivations. The isometric feature mapping (ISOMAP) technique reveals the intrinsic data structure, helps with recognising the involved neurons and, simultaneously, identifies the overlaps. Exemplar activation patterns are first estimated for all detected neurons and consecutively used to build a synthetic database in which spike overlaps are systematically varied and realistic noise is added. An Extreme Learning Machine (ELM) is then trained with the ISOMAP representation of this database and learns to associate the synthesised waveforms with the corresponding source neurons. The trained ELM is finally applied to the actual overlaps from the experimental data and this completes the entire spike-sorting process. Our approach is better characterised as semi-supervised, noise-assisted strategy of an empirical nature. The users engagement is restricted at recognising the number of active neurons from low-dimensional point-diagrams and at deciding about the complexity of overlaps. Efficiency is inherited from the incorporation of well-established algorithms. Moreover, robustness is guaranteed by adaptation to the actual noise properties of a given data set. The validity of our work has been verified via extensive experimentation, using realistically simulated data, under different levels of noise.

Towards the bio-personalization of music recommendation systems

Mobile neuroimaging leads to a whole new era of brain-oriented consumer applications.A single-sensor EEG biomarker to assess subjective music preference is introduced.It can facilitate the bio-personalization of modern music recommendation systems. Recent advances in biosensors technology and mobile electroencephalographic (EEG) interfaces have opened new application fields for cognitive monitoring. A computable biomarker for the assessment of spontaneous aesthetic brain responses during music listening is introduced here. It derives from well-established measures of cross-frequency coupling (CFC) and quantifies the music-induced alterations in the dynamic relationships between brain rhythms. During a stage of exploratory analysis, and using the signals from a suitably designed experiment, we established the biomarker, which acts on brain activations recorded over the left prefrontal cortex and focuses on the functional coupling between high-β and low-γ oscillations. Based on data from an additional experimental paradigm, we validated the introduced biomarker and showed its relevance for expressing the subjective aesthetic appreciation of a piece of music. Our approach resulted in an affordable tool that can promote human-machine interaction and, by serving as a personalized music annotation strategy, can be potentially integrated into modern flexible music recommendation systems.

Spontaneous Up states in vitro: a single-metric index of the functional maturation and regional differentiation of the cerebral cortex

Understanding the development and differentiation of the neocortex remains a central focus of neuroscience. While previous studies have examined isolated aspects of cellular and synaptic organization, an integrated functional index of the cortical microcircuit is still lacking. Here we aimed to provide such an index, in the form of spontaneously recurring periods of persistent network activity -or Up states- recorded in mouse cortical slices. These coordinated network dynamics emerge through the orchestrated regulation of multiple cellular and synaptic elements and represent the default activity of the cortical microcircuit. To explore whether spontaneous Up states can capture developmental changes in intracortical networks we obtained local field potential recordings throughout the mouse lifespan. Two independent and complementary methodologies revealed that Up state activity is systematically modified by age, with the largest changes occurring during early development and adolescence. To explore possible regional heterogeneities we also compared the development of Up states in two distinct cortical areas and show that primary somatosensory cortex develops at a faster pace than primary motor cortex. Our findings suggest that in vitro Up states can serve as a functional index of cortical development and differentiation and can provide a baseline for comparing experimental and/or genetic mouse models.

In quest of the missing neuron: Spike sorting based on dominant-sets clustering

Spike sorting algorithms aim at decomposing complex extracellular signals to independent events from single neurons in the electrodes vicinity. The decision about the actual number of active neurons is still an open issue, with sparsely firing neurons and background activity the most influencing factors. We introduce a graph-theoretical algorithmic procedure that successfully resolves this issue. Dimensionality reduction coupled with a modern, efficient and progressively executable clustering routine proved to achieve higher performance standards than popular spike sorting methods. Our method is validated extensively using simulated data for different levels of SNR.

A Consumer BCI for Automated Music Evaluation Within a Popular On-Demand Music Streaming Service “Taking Listener’s Brainwaves to Extremes”

We investigated the possibility of a using a machine-learning scheme in conjunction with commercial wearable EEG-devices for translating listener’s subjective experience of music into scores that can be used for the automated annotation of music in popular on-demand streaming services.

Spike Sorting based on Dominant-Sets clustering

Spike sorting algorithms aim at decomposing complex extracellularly recorded electrical signals to independent events from single neurons in the vicinity of electrode. The decision about the actual number of active neurons in a neural recording is still an open issue, with sparsely firing neurons and background activity the most influencing factors. We introduce a graph-theoretical algorithmic procedure that successfully resolves this issue. Dimensionality reduction coupled with a modern, efficient and progressively-executable clustering routine proved to achieve higher performance standards than popular spike sorting methods. Our method is validated extensively using simulated data for different levels of SNR.

Towards an affordable brain computer interface for the assessment of programmers’ mental workload

Abstract This paper provides a proof of concept for the use of wearable technology, and specifically wearable Electroencephalography (EEG), in the field of Empirical Software Engineering. Particularly, we investigated the brain activity of Software Engineers (SEngs) while performing two distinct but related mental tasks: understanding and inspecting code for syntax errors. By comparing the emerging EEG patterns of activity and neural synchrony, we identified brain signatures that are specific to code comprehension. Moreover, using the programmers rating about the difficulty of each code snippet shown, we identified neural correlates of subjective difficulty during code comprehension. Finally, we attempted to build a model of subjective difficulty based on the recorded brainwave patterns. The reported results show promise towards novel alternatives to programmers’ training and education. Findings of this kind may eventually lead to various technical and methodological improvements in various aspects of software development like programming languages, building platforms for teams, and team working schemes.

Musical NeuroPicks: A consumer-grade BCI for on-demand music streaming services

We investigated the possibility of using a machine-learning scheme in conjunction with commercial wearable EEG-devices for translating listeners subjective experience of music into scores that can be used in popular on-demand music streaming services. Our study resulted into two variants, differing in terms of performance and execution time, and hence, subserving distinct applications in online streaming music platforms. The first method, NeuroPicks, is extremely accurate but slower. It is based on the well-established neuroscientific concepts of brainwave frequency bands, activation asymmetry index and cross frequency coupling (CFC). The second method, NeuroPicksVQ, offers prompt predictions of lower credibility and relies on a custom-built version of vector quantization procedure that facilitates a novel parameterization of the music-modulated brainwaves. Beyond the feature engineering step, both methods exploit the inherent efficiency of extreme learning machines (ELMs) so as to translate, in a personalized fashion, the derived patterns into a listeners score. NeuroPicks method may find applications as an integral part of contemporary music recommendation systems, while NeuroPicksVQ can control the selection of music tracks. Encouraging experimental results, from a pragmatic use of the systems, are presented.