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“Everyone,” I improvised, “let’s forget about finding answers for today and just come up with some new questions we could be asking about this problem. Let’s see how many we can write down in the time we have left.” The students dutifully started to throw out questions, and I scribbled them on a chalkboard, redirecting anybody who started to suggest an answer. To my surprise, the room was quickly energized. At the end of the session, people left talking excitedly about a few of the questions that had emerged—those that challenged basic assumptions we had been making. For instance: Were there grassroots efforts we could support, rather than handing down rules from the top? And: What could we learn from pockets within our own organization that had achieved equality, instead of automatically looking elsewhere for best practices? Suddenly, there was much more to discuss, because we had opened up unexpected pathways to potential solutions.

Brainstorming for questions, not answers, wasn’t something I’d tried before. It just occurred to me in that moment, probably because I had recently been reading sociologist Parker Palmer’s early work about creative discovery through open, honest inquiry. But this technique worked so well with the students that I began experimenting with it in consulting engagements, and eventually it evolved into a methodology that I continue to refine. By now I’ve used it with hundreds of clients, including global teams at Chanel, Danone, Disney, EY, Fidelity, Genentech, Salesforce, and dozens of other companies; nonprofit organizations; and individual leaders I’ve coached.

Underlying the approach is a broader recognition that fresh questions often beget novel—even transformative—insights. Consider this example from the field of psychology: Before 1998 virtually all well-trained psychologists focused on attacking the roots of mental disorders and deficits, on the assumption that well-being came down to the absence of those negative conditions. But then Martin Seligman became president of the American Psychological Association, and he reframed things for his colleagues. What if, he asked in a speech at the APA’s annual meeting, well-being is just as driven by the presence of certain positive conditions—keys to flourishing that could be recognized, measured, and cultivated? With that question, the positive psychology movement was born.

Brainstorming for questions rather than answers makes it easier to push past cognitive biases and venture into uncharted territory. We’ve seen this dynamic in academic studies—in social psychologist Online Cheapest floral cutout laceup shoes Black Emporio Armani Cheap Latest Clearance Collections DJvnm8MM
on the power of reframing during times of transition, for instance. Yet lingering in a questioning mode doesn’t come naturally to most people, because we’re conditioned from an early age to just keep the answers coming.

The methodology I’ve developed is essentially Cheap Sale Purchase Preowned Leather flats Saint Laurent Discount Footlocker Pictures Comfortable oEKw4J
in valuable new ways. It helps people adopt a more creative habit of thinking and, when they’re looking for breakthroughs, gives them a sense of control. There’s actually something they can do other than sit and wait for a bolt from the blue. Here, I’ll describe how and why this approach works. You can use it anytime you (in a group or individually) are feeling stuck or trying to imagine new possibilities. And if you make it a regular practice in your organization, it can foster a stronger culture of collective problem solving and truth seeking.

Software updates

Digitize the position of the EEG electrodes and the subject's head shape |

Support for multiple modalities: , , , , Marvel crystal pumos Black Jimmy Choo London Clearance Online Bhtw7e

Read data from the most popular file formats |

Interactive access to data files in native formats |

Import data in Matlab |

Import and order data in a well-organized database |

Review, edit and import event markers in continuous recordings |

Automatic detection of well-defined artifacts: eye blinks, heartbeats |

Artifact correction: Signal Space Projections ()

Independent Component Analysis ()

Detection of bad trials / bad channels

Baseline correction |

Power spectrum density |

Frequency filtering, resampling |

Epoching |

Averaging |

Various time series displays |

Data mapping on 2D or 3D surfaces |

Generate slides and animations (export as contact sheets , snapshots , movies , ...)

Flexible montage editor |

Channel selection and sensor clustering |

Generate surfaces from MRI volume: head, inner skull and outer skull |

Use individual or template anatomy (MNI / Colin27 or ICBM152 brain) |

Template anatomy can be warped to individual head surface |

Import MRI volumes and tessellated surface envelopes |

Automatic or interactive co-registration with the MEG/EEG coordinate system |

Volume rendering (multiple display modes) |

Anatomical atlases: surface parcelations and sub-cortical regions |

Apply the same process to many files in a few clicks |

Automatic generation of scripts to perform full analysis |

Flexible plug-in structure that makes the software easy to extend |

MEG: Single sphere, overlapping spheres |

EEG: Berg's three-layer sphere, Boundary Element Models (with OpenMEEG )

Estimation of noise statistics for improved source modeling |

L2 Minimum-norm current estimates |

Normalizations: dSPM, sLORETA, Z-score |

Dipole scanning |

Dipole fitting with FieldTrip |

Import and display of Neuromag's Xfit and CTF's DipoleFit dipole models |

Simulation of MEG/EEG recordings from source activity |

Multiple options for surface and volume rendering of the source maps |

Definition of regions of interest |

Project the sources on a surface with higher or lower resolution |

Project the sources on a group template |

Surface or volume spatial smoothing |

Time-frequency analyses of sensor data and sources time series using Morlet wavelet, Fast Fourier Transform and Hilbert transform |

Registration of individual brains to a template |

Parametric and non-parametric statistics |

Pad each side by 0 to 20 percent. This adds new pixels to the sides. These pixels will either be filled with a constant value (mode=constant) or filled with the value on the closest edge (mode=edge). If a constant value is used, it will be a random value between 0 and 128 (sampled per image).

Pad the top side of each image by 0 to 30 pixels, the right side by 0-10px, bottom side by 0-30px and left side by 0-10px. Use any of the available modes to fill new pixels and if the mode is constant then use a constant value between 0 and 128.

Crop/pad each side by up to 10px. The value will be sampled once per image and used for all sides (i.e. all sides gain/lose the same number of rows/colums).


Augmenter that pads images, i.e. adds columns/rows to them.

This is a proxy for . It only accepts positive pixel/percent values.


Augmenter that crops/cuts away pixels at the sides of the image.

This is a proxy for . It only accepts positive pixel/percent values and transfers them as negative values to .

Flip/mirror input images horizontally.

Flip 50% of all images horizontally:

Flip/mirror input images vertically.

Flip 50% of all images vertically:

Completely or partially transform images to their superpixel representation.

Generate about 64 superpixels per image. Replace each one with a probability of 50% by its average pixel color.

Generate 16 to 128 superpixels per image. Replace each superpixel with a probability between 10 and 100% (sampled once per image) by its average pixel color.

Effect of setting n_segments to a fixed value of 64 and then increasing p_replace from 0.0 and 1.0:

Effect of setting p_replace to a fixed value of 1.0 and then increasing n_segments from 1*16 to 9*16=144:

Augmenter to change the colorspace of images.

The following example shows how to change the colorspace from RGB to HSV, then add 50-100 to the first channel, then convert back to RGB. This increases the hue value of each image.

Augmenter to convert images to their grayscale versions.

Change images to grayscale and overlay them with the original image by varying strengths, effectively removing 0 to 100% of the color:

Visualization of increasing alpha from 0.0 to 1.0 in 8 steps:

Augmenter to blur images using gaussian kernels.

Blur each image with a gaussian kernel with a sigma of 3.0 :

Blur an image by computing simple means over neighbourhoods.

Blur each image using a mean over neihbourhoods that have a random size between 2x2 and 11x11:




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