Pattern Rec: Enlightenment

I've been trying to understand Pattern Rec class today and I finally got it. The example given today could have very well been my EEG data. We will know WHAT the subject's intension is by the presentation of the stimuli (left, right, up, down). We will also know WHEN the stimuli will be presented so we can assume shortly after the presentation is the EEG representation of that intention. Now the time segments of eeg (during the presentation of stimuli) will be my PATTERNS and the classification TARGETS will be from 1 - 5 (left, right, up, down and null) . With the data separated out like this I think I can run it thru any classifier.


Bill, I finally get what you were talking about. If you tell the user to make the wrong movement with a controller will the brain still move toward the stimuli. Am I right?

eeglab

http://www.sccn.ucsd.edu/eeglab/

EEGLAB: an open source toolbox for analysis of single-trial EEG ...

tinkering with eeglab today



was able to import a .edf file and export a .txt file.

• can't import whole file at once TOO LARGE
• separate file [0 100],[101 200], ...

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try different file time segments
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try .bdf tonight
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Getting ready to

Paper: Towards adaptive classification for BCI

A Tutorial?

• Purpose of the paper:
• Non-stationarities are ubiquitous in EEG signals.
• (a) in the differences between the initial calibration measurement and the online operation of a BCI,
  
• (b) caused by changes in the subject’s brain processes during an experiment (e.g. due to fatigue, change of task involvement, etc)
• we quantify for the first time such systematic evidence of statistical differences in data recorded during offline and online sessions.
• we propose novel techniques of investigating and visualizing data distributions, which are particularly useful for the analysis of (non-)stationarities
• Our study shows that the brain signals used for control can change substantially from the offline calibration sessions to online control, and also within a single session.
• we propose several adaptive classification schemes and study their performance on
  data recorded during online experiments.
• Classification methods
• ORIG: this is the unmodified classifier trained on data from the offline scenario and serves as a baseline.
  
• REBIAS:we use the continuous output of the unmodified classifier and shift the output by an amount that would minimize the error on the labeled feedback data.
  
• RETRAIN:we use the features as chosen from the offline scenario, but retrain the LDA classifier to choose the hyper plane that minimizes the error on labeled feedback data.
  
• RECSP:we completely ignore the offline training data and perform CSP feature selection and classification training solely on the feedback data.
  
• Types and controls
• (1) all the labeled online data up to the current point (cumulative),
  
• (2) only a window over the immediate past (moving), or
  
• (3) only an initial window of data from each session(initial).
  
• We thus have C-REBIAS7, C-RETRAIN and C-RECSP, W-REBIAS, W-RETRAIN and W-RECSP, and I-REBIAS, I-RETRAIN and I-RECSP, respectively, for the three cases considered.
  
  
  

Paper: Induced sensorimortorrhythms during movement observation and motor imagery

• Interesting but not immediately relevant
  
• the visual representation of the observed action onto the motor representation of the same action“
• 9 subjects
• 
  

Paper: An efficient rhythmic component expression and weighting synthesis strategy for classifying motor imagery EEG in a brain–computer interface

• It is important to identify robust feature descriptions for BCI research, either in the time or frequency domain as derived from single-trial motor imagery (MI) EEG data
• Autoregressive models are commonly used in terms of model
  parameters without direct relation to specific brain activities
  (Anderson et al 1998, Pfurtscheller et al 1998).
• Time- domain features, such as activity, mobility and complexity, describing the properties of a signal trial have been used (Obermaier et al 2001).
  
• A direct ERD estimation calculating the energies within pieces of time windows imposed on
  EEGs at pre-selected frequency bands has been found to be a good feature in classifying right- or left-hand imagined movements (Pfurtscheller et al 1997).
  
• The primary drawback of scalp EEG is the spatial smearing
  and low signal-to-noise ratio.
• Since there are correlations among the recording electrodes, spatial filters have been used as means of accentuating localized activity and reducing diffused activity
• McFarland et al (1997) have compared the EEG classification results using different spatial filters and concluded that the common average and the Laplacian derivation yield good performance.
  
• Fifth order IIR Butterworth band-pass filters were used for
  frequency decomposition
• Two important timing cues that should be mentioned here are that at 3.75 s of a trial period,
  
• a cue (preparation cue) of one letter (L or R) appeared on the screen indicating which hand (specifically index finger) movement should be imagined;
  
• and at 5.0 s another cue (execution cue) appeared, indicating that it was time to make the requested response. The subjects were well trained to consistently respond to the cue signals within 100 ms.
  

Paper: Boosting bit rates and error detection for the classification of fast-paced motor commands based on single-trial EEG analysis

• GREAT PATTERN REC PAPER!!!
• The required discrimination of different brain states may be based on evoked poten-
  tials (like steady-state visual evoked potentials or P300) a motor output, i.e., self-paced typewriting on a computer keyboard.
• This Paper
• (a) We exploit advanced machine learning and signal processing technol-
  ogy for single-trial EEG evaluation requiring no prior sub-
  ject training.
  
• (b) We use slow pre-movement potentials as
  physiological signals, and
  
• (c) we utilize a fast-paced experimental paradigm.
  
• "Let the machine learn" so they chose any every day task
• experiment (paradim)
  
• We let our sub jects (all without neurological deficit) make a binary (left/right hand) decision coupled to a motor output, i.e., self-paced typewriting on a computer
  keyboard.
• Using multi-channel scalp EEG recordings, we
  analyze the single-trial differential potential distributions
  of the Bereitschaftspotential (BP) preceding voluntary (left
  or right hand) finger movements over the corresponding
  (right/left) primary motor cortex.
• non-oscillatory event-related potentials (ERPs) two reasons:
• (physiological) if a bci is monitoring an idling rhythm then a prerequisite is stable presence of that rhythm. This could become a problem at a fast pace because the signal might not have the time to fully recover or idle again (my words pg.2)
• (data analysis) How to classify the noisy, high-demensional EEG data? The difference between task-related brain activity and non-task-related activity might not be enough (my words pg.2)
• Robust Preprocessing, Distro of ERPs, and Classification section
• 8 subjects
• Fisher Discriminant (again)
• GREAT PATTERN REC PAPER!!!
• 
  

Paper: Classifying Single Trial EEG: Towards BCI

Classifying Single Trial EEG: Towards BCI

• single trial is difficult for signal processing and machine learning
• This study a pseudo-online evaluation of single-trial EEGs from voluntary self-paced finger movements and exploit the laterality of the left/right hand signal as one bit of information for later control
• Two key issues to start with when conceiving a BCI are
  
• (1) the definition of a behavioral context in which a subject’s brain signals will be monitored and used eventually as surrogate for a bodily, e.g., manual, input of computer commands, and
  
• (2) the choice of brain signals which are optimally capable to
  convey the subject’s intention to the computer.
• Types of experiments: two alternative force reaction VS endogeneous (natural reaction)
• a negative ›Bereitschaftspotential‹ (BP) precedes the voluntary initiation of the movement.
• A differential scalp potential distribution can be reliably demonstrated in a majority of experimental subjects with larger BP at lateral scalp positions (C3, C4) positioned over the left or right hemispherical primary motor cortex, respectively, consistenly correlating with the performing (right or left) hand
• While it is true thatbrain potentials comparable to BP are associated with an imagination of hand movements, which indeed is consistent with the assumption that the primary motor cortex is active with motor imagery, actual motor performance significantly increased these potentials
• Reason 4actually perform the typewriting finger movements, rather than to merely imagine their performance, for two reasons:
• (1)this will increase the BP signal strength optimising the signal-to-noise ratio in BCI-related single trial analyses; and
  
• (2)we propose that it is important for the subject’s task efficiency not to be engaged in an unnatural condition where, in addition to the preparation of a
  motor command, a second task, i.e., to ›veto‹ the very same movement, has to be executed.
  
• ADD SECTION: slow cortical potentials (SCP) and how they can be self-regulated in a feedback scenario
• Typical preprocessing techniques are adaptive autoregressive parameters, common spatial patterns (after band pass filtering) and band power in subject specific frequency bands. Classification is done by Fisher discriminant analysis, multi-layer neural networks or LVQ variants
• Linear methods (pg 4): Fisher Discriminant, Regular Fisher Discriminant, Sparse Fisher Discriminant, Support Vector Machine, K-Nearest Neighbor, Classification of response-aligned windows, Classificationinslidingwindows, Movementdetectionandpseudo-onlineclassification,
  
• Conclusion, High classification rate with low errors