Single-trial extraction of event-related potentials (ERPs) and classification of visual stimuli by ensemble use of discrete wavelet transform with Huffman coding and machine learning techniques.

BACKGROUND: Presentation of visual stimuli can induce changes in EEG signals that are typically detectable by averaging together data from multiple trials for individual participant analysis as well as for groups or conditions analysis of multiple participants. This study proposes a new method based on the discrete wavelet transform with Huffman coding and machine learning for single-trial analysis of evenal (ERPs) and classification of different visual events in the visual object detection task. METHODS: EEG single trials are decomposed with discrete wavelet transform (DWT) up to the [Formula: see text] level of decomposition using a biorthogonal B-spline wavelet. The coefficients of DWT in each trial are thresholded to discard sparse wavelet coefficients, while the quality of the signal is well maintained. The remaining optimum coefficients in each trial are encoded into bitstreams using Huffman coding, and the codewords are represented as a feature of the ERP signal. The performance of this method is tested with real visual ERPs of sixty-eight subjects. RESULTS: The proposed method significantly discards the spontaneous EEG activity, extracts the single-trial visual ERPs, represents the ERP waveform into a compact bitstream as a feature, and achieves promising results in classifying the visual objects with classification performance metrics: accuracies 93.60[Formula: see text], sensitivities 93.55[Formula: see text], specificities 94.85[Formula: see text], precisions 92.50[Formula: see text], and area under the curve (AUC) 0.93[Formula: see text] using SVM and k-NN machine learning classifiers. CONCLUSION: The proposed method suggests that the joint use of discrete wavelet transform (DWT) with Huffman coding has the potential to efficiently extract ERPs from background EEG for studying evoked responses in single-trial ERPs and classifying visual stimuli. The proposed approach has O(N) time complexity and could be implemented in real-time systems, such as the brain-computer interface (BCI), where fast detection of mental events is desired to smoothly operate a machine with minds.

Exploiting Information in Event-Related Brain Potentials from Average Temporal Waveform, Time-Frequency Representation, and Phase Dynamics.

Characterizing the brain's dynamic pattern of response to an input in electroencephalography (EEG) is not a trivial task due to the entanglement of the complex spontaneous brain activity. In this context, the brain's response can be defined as (1) the additional neural activity components generated after the input or (2) the changes in the ongoing spontaneous activities induced by the input. Moreover, the response can be manifested in multiple features. Three commonly studied examples of features are (1) transient temporal waveform, (2) time-frequency representation, and (3) phase dynamics. The most extensively used method of average event-related potentials (ERPs) captures the first one, while the latter two and other more complex features are attracting increasing attention. However, there has not been much work providing a systematic illustration and guidance for how to effectively exploit multifaceted features in neural cognitive research. Based on a visual oddball ERPs dataset with 200 participants, this work demonstrates how the information from the above-mentioned features are complementary to each other and how they can be integrated based on stereotypical neural-network-based machine learning approaches to better exploit neural dynamic information in basic and applied cognitive research.

Local networks from different parts of the human cerebral cortex generate and share the same population dynamic.

A major goal of neuroscience is to reveal mechanisms supporting collaborative actions of neurons in local and larger-scale networks. However, no clear overall principle of operation has emerged despite decades-long experimental efforts. Here, we used an unbiased method to extract and identify the dynamics of local postsynaptic network states contained in the cortical field potential. Field potentials were recorded by depth electrodes targeting a wide selection of cortical regions during spontaneous activities, and sensory, motor, and cognitive experimental tasks. Despite different architectures and different activities, all local cortical networks generated the same type of dynamic confined to one region only of state space. Surprisingly, within this region, state trajectories expanded and contracted continuously during all brain activities and generated a single expansion followed by a contraction in a single trial. This behavior deviates from known attractors and attractor networks. The state-space contractions of particular subsets of brain regions cross-correlated during perceptive, motor, and cognitive tasks. Our results imply that the cortex does not need to change its dynamic to shift between different activities, making task-switching inherent in the dynamic of collective cortical operations. Our results provide a mathematically described general explanation of local and larger scale cortical dynamic.

Emerging principles of spacetime in brains: Meeting report on spatial neurodynamics.

How do neurons and networks of neurons interact spatially? Here, we overview recent discoveries revealing how spatial dynamics of spiking and postsynaptic activity efficiently expose and explain fundamental brain and brainstem mechanisms behind detection, perception, learning, and behavior.

A Novel, Fast, Reliable, and Data-Driven Method for Simultaneous Single-Trial Mining and Amplitude-Latency Estimation Based on Proximity Graphs and Network Analysis.

Both amplitude and latency of single-trial EEG/MEG recordings provide valuable information regarding functionality of the human brain. In this article, we provided a data-driven graph and network-based framework for mining information from multi-trial event-related brain recordings. In the first part, we provide the general outline of the proposed methodological approach. In the second part, we provide a more detailed illustration, and present the obtained results on every step of the algorithmic procedure. To justify the proposed framework instead of presenting the analytic data mining and graph-based steps, we address the problem of response variability, a prerequisite to reliable estimates for both the amplitude and latency on specific N/P components linked to the nature of the stimuli. The major question addressed in this study is the selection of representative single-trials with the aim of uncovering a less noisey averaged waveform elicited from the stimuli. This graph and network-based algorithmic procedure increases the signal-to-noise (SNR) of the brain response, a key pre-processing step to reveal significant and reliable amplitude and latency at a specific time after the onset of the stimulus and with the right polarity (N or P). We demonstrated the whole approach using electroencephalography (EEG) auditory mismatch negativity (MMN) recordings from 42 young healthy controls. The method is novel, fast and data-driven succeeding first to reveal the true waveform elicited by MMN on different conditions (frequency, intensity, duration, etc.). The proposed graph-oriented algorithmic pipeline increased the SNR of the characteristic waveforms and the reliability of amplitude and latency within the adopted cohort. We also demonstrated how different EEG reference schemes (REST vs. average) can influence amplitude-latency estimation. Simulation results revealed robust amplitude-latency estimations under different SNR and amplitude-latency variations with the proposed algorithm.

A new measure for monitoring intraoperative somatosensory evoked potentials.

OBJECTIVE: To propose a new measure for effective monitoring of intraoperative somatosensory evoked potentials (SEP) and to validate the feasibility of this measure for evoked potentials (EP) and single trials with a retrospective data analysis study. METHODS: The proposed new measure (hereafter, a slope-measure) was defined as the relative slope of the amplitude and latency at each EP peak compared to the baseline value, which is sensitive to the change in the amplitude and latency simultaneously. We used the slope-measure for EP and single trials and compared the significant change detection time with that of the conventional peak-to-peak method. When applied to single trials, each single trial signal was processed with optimal filters before using the slope-measure. In this retrospective data analysis, 7 patients who underwent cerebral aneurysm clipping surgery for unruptured aneurysm middle cerebral artery (MCA) bifurcation were included. RESULTS: We found that this simple slope-measure has a detection time that is as early or earlier than that of the conventional method; furthermore, using the slope-measure in optimally filtered single trials provides warning signs earlier than that of the conventional method during MCA clipping surgery. CONCLUSION: Our results have confirmed the feasibility of the slope-measure for intraoperative SEP monitoring. This is a novel study that provides a useful measure for either EP or single trials in intraoperative SEP monitoring.

Healthy aging delays scalp EEG sensitivity to noise in a face discrimination task.

We used a single-trial ERP approach to quantify age-related changes in the time-course of noise sensitivity. A total of 62 healthy adults, aged between 19 and 98, performed a non-speeded discrimination task between two faces. Stimulus information was controlled by parametrically manipulating the phase spectrum of these faces. Behavioral 75% correct thresholds increased with age. This result may be explained by lower signal-to-noise ratios in older brains. ERP from each subject were entered into a single-trial general linear regression model to identify variations in neural activity statistically associated with changes in image structure. The fit of the model, indexed by R(2), was computed at multiple post-stimulus time points. The time-course of the R(2) function showed significantly delayed noise sensitivity in older observers. This age effect is reliable, as demonstrated by test-retest in 24 subjects, and started about 120 ms after stimulus onset. Our analyses suggest also a qualitative change from a young to an older pattern of brain activity at around 47 ± 4 years old.