Blind separation of auditory event-related brain responses into independent components

Abstract

Averaged event-related potential (ERP) data recorded from the human scalp reveal electroencephalographic (EEG) activity that is reliably time-locked and phase-locked to experimental events. We report here the application of a method based on information theory that decomposes one or more ERPs recorded at multiple scalp sensors into a sum of components with fixed scalp distributions and sparsely activated, maximally independent time courses. Independent component analysis (ICA) decomposes ERP data into a number of components equal to the number of sensors. The derived components have distinct but not necessarily orthogonal scalp projections. Unlike dipole-fitting methods, the algorithm does not model the locations of their generators in the head. Unlike methods that remove second-order correlations, such as principal component analysis (PCA), ICA also minimizes higher-order dependencies. Applied to detected-and undetected-target ERPs from an auditory vigilance experiment, the algorithm derived ten components that decomposed each of the major response peaks into one or more ICA components with relatively simple scalp distributions. Three of these components were active only when the subject detected the targets, three other components only when the target went undetected, and one in both cases. Three additional components accounted for the steady-state brain response to a 39-Hz background click train. Major features of the decomposition proved robust across sessions and changes in sensor number and placement. This method of ERP analysis can be used to compare responses from multiple stimuli, task conditions, and subject states.

Figures

Figure 1

Schematic overview of ICA of EEG data. (A) (Upper) Averaged (or single) EEG epochs, x, recorded from multiple scalp sites are used to train an “unmixing” weight matrix, W, so as to maximize the entropy of the nonlinearly transformed output, g(Wx). (Lower) After training, rows of the trained weight matrix, W, are linear spatial filters decomposing the input data into the independent activities of the ICA components. Rows of the product of W and the input data, x, are the activation waveforms of the ICA components, while columns of the inverse weight matrix, W−1, map their projections onto the scalp electrodes. (B) (Inset) Schematic illustration of ICA decompositions of a simulated evoked response, recorded at two electrodes (A and B), summing the activity of two temporally independent response sources (#1 and #2) with arbitrary (focal or diffuse) spatial distributions. (Upper) Scatterplot of potentials recorded at the two electrodes, showing the response as a two-dimensional trajectory. In this plot, the activity of source #1 alone would lie on the near-vertical axis ICA-1; the activity of source #2 alone would lie on the near-horizontal (but not orthogonal) axis ICA-2. If the time courses of activation of the two brain networks are independent of one another, the summed output of sources #1 and #2 will, over time, fill the dashed parallelogram. The first principal component of the data (PCA-1) indicates the direction of maximum data variance, but neither this nor the second principal component orthogonal to it identifies either of the independent components. (Lower) The ICA algorithm finds the directions of the two axes (ICA-1, ICA-2) by maximizing the entropy of the data linearly transformed to the ICA component axes and nonlinearly transformed using a logistic sigmoid. The linear transformation and sigmoidal nonlinearity rotates and spreads the data to fill the dashed square as evenly as possible, whereas in the original (A, B) space (above), the data remain within an oblique parallelogram.

Figure 2

Decomposition of an ERP data set. (A) Averaged evoked responses at 14 scalp channels from one subject in a sustained auditory detection experiment (23) to detected (blue traces, 209 epochs) and undetected (red traces, 81 epochs) slow-onset noise-burst targets. (B) Activation wave forms of the resulting 14 ICA components during the detected (blue traces) and undetected (red traces) response epochs. Seven components (ICA-1 to ICA-7) are predominantly activated for a period of 50–300 ms during one or the other response. Three more ICA components (ICA-8 to ICA-10) compose the auditory SSR (22) to a click train presented throughout the experiment at one-eighth the EEG sampling rate. The remaining four ICA components (ICA-11 to ICA-14) presumably sum activity of multiple weak brain and extra-brain sources.

Figure 3

Scalp distributions of the ICA components. (A) Projected activity of components ICA-1 to ICA-4 (colored traces) superimposed on the scalp wave forms of the detected-target response (black traces) together with interpolated topographic maps of the component projections (25). Component ICA-2 (green traces) accounts for the central parietal positivity near 450 ms (labeled P3) as well as the concurrent prefrontal positivity at Fpz, whereas the central negativity near 400 ms (labeled N2) includes the activity of component ICA-4 (red traces) which has a different scalp distribution (map scaling ± 6 μV). (B) Projected scalp activity of components ICA-4 to ICA-7 (colored traces) superimposed on the scalp wave forms of the undetected-target response (black traces). The positive central peak near 300 ms (labeled P2) is accounted for by a single component ICA-4 (red traces), whereas the succeeding frontal negativity (labeled N2) is decomposed by the algorithm into three other components (ICA-5 to ICA-7) having central, frontal, and periocular topographies, respectively (map scaling ± 12 μV). (C) The ICA algorithm decomposes the 39-Hz auditory SSR in the detected-target response into three components (ICA-8 to ICA-10) derived from the detected-target ERP (Fig. 2A) by averaging 39 successive 25.6-ms (8-point) ERP time segments. The leftmost traces show the whole SSR at all 14 channels, the right traces, the projected time wave forms and scalp projections (scaled individually) of the three ICA components. The largest component, ICA-8, has a bilateral frontotemporal scalp distribution, as expected (26), while component ICA-9 has a bilateral parietal scalp distribution and component ICA-10 projects mainly to EOG and prefrontal channels. (D) Time courses and scalp topographies of corresponding ICA-2 components obtained in separate decompositions of detected-target responses in two separate sessions from two subjects (right columns) and from the grand-mean detected-target response for 11 subjects (left column). Note the nearly identical time courses (right traces) and scalp maps.