Add files using upload-large-folder tool

.gitattributes

@@ -57,3 +57,49 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+data/910_1_PD_REST.mat filter=lfs diff=lfs merge=lfs -text
+data/912_1_PD_REST.mat filter=lfs diff=lfs merge=lfs -text
+data/913_1_PD_REST.mat filter=lfs diff=lfs merge=lfs -text
+data/911_1_PD_REST.mat filter=lfs diff=lfs merge=lfs -text
+data/914_1_PD_REST.mat filter=lfs diff=lfs merge=lfs -text
+data/801_1_PD_REST.mat filter=lfs diff=lfs merge=lfs -text
+data/802_2_PD_REST.mat filter=lfs diff=lfs merge=lfs -text
+data/801_2_PD_REST.mat filter=lfs diff=lfs merge=lfs -text
+data/8010_1_PD_REST.mat filter=lfs diff=lfs merge=lfs -text
+data/802_1_PD_REST.mat filter=lfs diff=lfs merge=lfs -text
+data/803_1_PD_REST.mat filter=lfs diff=lfs merge=lfs -text
+data/804_1_PD_REST.mat filter=lfs diff=lfs merge=lfs -text
+data/804_2_PD_REST.mat filter=lfs diff=lfs merge=lfs -text
+data/805_2_PD_REST.mat filter=lfs diff=lfs merge=lfs -text
+data/805_1_PD_REST.mat filter=lfs diff=lfs merge=lfs -text
+data/803_2_PD_REST.mat filter=lfs diff=lfs merge=lfs -text
+data/8060_1_PD_REST.mat filter=lfs diff=lfs merge=lfs -text
+data/806_1_PD_REST.mat filter=lfs diff=lfs merge=lfs -text
+data/806_2_PD_REST.mat filter=lfs diff=lfs merge=lfs -text
+data/8070_1_PD_REST.mat filter=lfs diff=lfs merge=lfs -text
+data/807_1_PD_REST.mat filter=lfs diff=lfs merge=lfs -text
+data/807_2_PD_REST.mat filter=lfs diff=lfs merge=lfs -text
+data/808_1_PD_REST.mat filter=lfs diff=lfs merge=lfs -text
+data/808_2_PD_REST.mat filter=lfs diff=lfs merge=lfs -text
+data/809_1_PD_REST.mat filter=lfs diff=lfs merge=lfs -text
+data/809_2_PD_REST.mat filter=lfs diff=lfs merge=lfs -text
+data/810_1_PD_REST.mat filter=lfs diff=lfs merge=lfs -text
+data/810_2_PD_REST.mat filter=lfs diff=lfs merge=lfs -text
+data/811_1_PD_REST.mat filter=lfs diff=lfs merge=lfs -text
+data/811_2_PD_REST.mat filter=lfs diff=lfs merge=lfs -text
+data/813_1_PD_REST.mat filter=lfs diff=lfs merge=lfs -text
+data/814_1_PD_REST.mat filter=lfs diff=lfs merge=lfs -text
+data/813_2_PD_REST.mat filter=lfs diff=lfs merge=lfs -text
+data/814_2_PD_REST.mat filter=lfs diff=lfs merge=lfs -text
+data/815_1_PD_REST.mat filter=lfs diff=lfs merge=lfs -text
+data/816_1_PD_REST.mat filter=lfs diff=lfs merge=lfs -text
+data/815_2_PD_REST.mat filter=lfs diff=lfs merge=lfs -text
+data/816_2_PD_REST.mat filter=lfs diff=lfs merge=lfs -text
+data/817_1_PD_REST.mat filter=lfs diff=lfs merge=lfs -text
+data/817_2_PD_REST.mat filter=lfs diff=lfs merge=lfs -text
+data/818_1_PD_REST.mat filter=lfs diff=lfs merge=lfs -text
+data/818_2_PD_REST.mat filter=lfs diff=lfs merge=lfs -text
+data/819_1_PD_REST.mat filter=lfs diff=lfs merge=lfs -text
+data/819_2_PD_REST.mat filter=lfs diff=lfs merge=lfs -text
+data/820_1_PD_REST.mat filter=lfs diff=lfs merge=lfs -text
+data/820_2_PD_REST.mat filter=lfs diff=lfs merge=lfs -text

LICENSE.txt

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PD_Rest_from_BV.m

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+%% PD Rest
+clear all; clc
+datalocation='Y:\EEG_Data\PDDys\EEG\Raw EEG Data\';   % Data are here
+savedir='Y:\EEG_Data\PDDys\EEG\Processed EEG Data\REST for PREDiCT\'; % Data go here
+cd(savedir);
+
+PDsx=[801 802 803 804 805 806 807 808 809 810 811 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829];
+CTLsx=[894 908 8010 890 891 892 893 895 896 897 898 899 900 901 902 903 904 905 906 907 909 910 911 912 913 914 8060 8070];
+
+% Data are 68 chans: 1=63 is EEG, 64 is VEOG, 66-68 is XYZ accelerometer on hand (varied L or R).  
+% Ref'd to CPz - - will want to retrieve that during re-referencing.  See below for code for that.
+% See bottom of script for the stimulus presentation script used to collect these data
+
+for subj=[PDsx,CTLsx]
+    for session=1:2;  % ON or OFF medication.  CTL only had 1 session
+        if (subj>850 && session==1) || subj<850  % If not ctl, do session 2
+            if ~(exist([num2str(subj),'_',num2str(session),'_PD_REST.mat'])==2); % If it doesn't already exist
+                disp(['Do Rest --- Subno: ',num2str(subj),'      Session: ',num2str(session)]); disp(' ');
+                                
+                % ----------Load BrainVision data
+                EEG = pop_loadbv(datalocation,[num2str(subj),'_',num2str(session),'_ODDBALL.vhdr']);
+                
+                % Note: 803 S1 is bad.  Don't use.
+                
+                % ----------Get Locs
+                locpath=('Y:\Programs\eeglab12_0_2_1b\plugins\dipfit2.2\standard_BESA\standard-10-5-cap385.elp');
+                EEG = pop_chanedit(EEG,    'lookup', locpath);
+                EEG = eeg_checkset( EEG );
+                
+                
+                % ---------- Get event types
+                % All data start with 1 min of eyes closed rest:
+                    % trigger 3 happens every 2 seconds
+                    % trigger 4 happens every 2 seconds
+                % Followed by 1 min of eyes open rest:
+                    % trigger 1 happens every 2 seconds
+                    % trigger 2 happens every 2 seconds
+                for ai=2:length(EEG.event); temp=EEG.event(ai).type; TYPES(ai)=str2num(temp(2:end)) ; clear temp; end
+                % For fun
+                UNIQUE_TYPES=unique(TYPES);
+                for bi=1:length(UNIQUE_TYPES); UNIQUE_TYPES_COUNT(bi)=sum(TYPES==UNIQUE_TYPES(bi)); end
+                clc; TRIGGERS=[UNIQUE_TYPES;UNIQUE_TYPES_COUNT] % Trigger type, Frequency
+                % OK, now find the last trigger of the rest
+                Rest_Triggers=find(TYPES<5);
+                Last_Rest_Trigger=EEG.event(Rest_Triggers(end)).latency;  % Last trigger in **samples**
+                Last_Rest_Trigger_ms=Last_Rest_Trigger .* (1/(EEG.srate/1000));
+                Last_Rest_Trigger_Seconds=floor(Last_Rest_Trigger_ms/1000)+4;  % +4 for a little buffer
+                
+                % Cut data
+                EEG = pop_select( EEG,'time',[0 Last_Rest_Trigger_Seconds] );
+
+                % ---------- Make sure we have all 2 mins of rest
+                clear UNIQUE* TYPES TRIGGERS
+                for ai=2:length(EEG.event); temp=EEG.event(ai).type; TYPES(ai)=str2num(temp(2:end)) ; clear temp; end
+                UNIQUE_TYPES=unique(TYPES);
+                for bi=1:length(UNIQUE_TYPES); UNIQUE_TYPES_COUNT(bi)=sum(TYPES==UNIQUE_TYPES(bi)); end
+                clc; TRIGGERS=[UNIQUE_TYPES;UNIQUE_TYPES_COUNT] % Trigger type, Frequency
+                % Start the count
+                N_Trigs=0;
+                for ci=1:4, N_Trigs=N_Trigs+UNIQUE_TYPES_COUNT(UNIQUE_TYPES==ci); end
+                if N_Trigs<120, BOOM; end  % BOOM just kills things
+                
+                % Note, 8070 only has a little bit of eyes closed data.
+                % Must have started recording too late.  
+                
+                % ---------- Save
+                save([savedir,num2str(subj),'_',num2str(session),'_PD_REST.mat'],'EEG');
+                
+                % ---------- Housekeeping
+                clear EEG UNIQUE* TYPES TRIGGERS
+                
+            end
+        end
+    end
+end
+
+BOOM;
+
+%% If you want, you could do some of this to make life easier:
+
+% ---------- Remove X,Y,Z & VEOG
+EEG.VEOG=squeeze(EEG.data(64,:,:));
+EEG.X=squeeze(EEG.data(65,:,:));
+EEG.Y=squeeze(EEG.data(66,:,:));
+EEG.Z=squeeze(EEG.data(67,:,:));
+EEG.data=EEG.data(1:63,:,:);
+EEG.nbchan=63;
+EEG.chanlocs(67)=[];  EEG.chanlocs(66)=[]; EEG.chanlocs(65)=[]; EEG.chanlocs(64)=[];
+% ---------- Add CPz
+EEG = pop_chanedit(EEG,'append',63,'changefield',{64 'labels' 'CPz'});
+EEG = pop_chanedit(EEG,'lookup', locpath);
+% ---------- Re-Ref to Average Ref and recover CPz
+EEG = pop_reref(EEG,[],'refloc',struct('labels',{'CPz'},'type',{''},'theta',{180},'radius',{0.12662},'X',{-32.9279},'Y',{-4.0325e-15},'Z',{78.363},...
+    'sph_theta',{-180},'sph_phi',{67.208},'sph_radius',{85},'urchan',{64},'ref',{''}),'keepref','on');
+% ---------- Remove CONSISTENLY BAD channels now that CPz has been reconstructed from the total
+EEG.MASTOIDS = squeeze(mean(EEG.data([10,21],:,:),1));
+EEG.data = EEG.data([1:4,6:9,11:20,22:26,28:64],:,:);
+EEG.nbchan=60;
+EEG.chanlocs(27)=[];  EEG.chanlocs(21)=[];   EEG.chanlocs(10)=[];   EEG.chanlocs(5)=[];  % Have to be in this order!
+% ---------- Re-ref to average again now that the contaminated channels are gone
+EEG = pop_reref(EEG,[]);
+% ---------- Remove mean
+EEG = pop_rmbase(EEG,[],[]);
+
+%%
+
+%  ~ SNIP
+
+% Experimenter instructions
+Screen('TextSize',wPtr,30);
+Screen('TextFont',wPtr,'Times');
+Screen('TextStyle',wPtr,0);
+Screen('TextColor',wPtr,[255 255 255]);
+beginningText1 = 'Experimenter: Continue';
+DrawFormattedText(wPtr,beginningText1,'center','center');
+Screen(wPtr, 'Flip');
+KbWait([],3); %Waits for keyboard(any) press
+
+instructionText = 'Welcome and thank you for participating in our study. \n\n\n We are going to start by recording some activities \n\n of your brain activity at rest.';
+DrawFormattedText(wPtr,instructionText,'center','center');
+Screen(wPtr, 'Flip');
+KbWait([],3); %Waits for keyboard(any) press
+
+instructionText = 'All you need to do is sit and rest quietly. \n\n\nWe will be recording resting EEG with your eyes \n\n\n CLOSED for the next minute. \n\n\n A tone will sound to let you know when \n\nthe time is up.';
+DrawFormattedText(wPtr,instructionText,'center','center');
+Screen(wPtr, 'Flip');
+KbWait([],3); %Waits for keyboard(any) press
+
+% CLOSED
+DrawFormattedText(wPtr,'Close Eyes','center','center'); Screen(wPtr, 'Flip'); WaitSecs(1);
+for ai=1:30
+    io64(ioObject,LTP1address,3);  WaitSecs(.05);  io64(ioObject,LTP1address,0); WaitSecs(.95);
+    io64(ioObject,LTP1address,4);  WaitSecs(.05);  io64(ioObject,LTP1address,0); WaitSecs(.95);
+end
+sound(Tone(1,:),echant);
+
+instructionText = 'Now another minute with your eyes OPEN.';
+DrawFormattedText(wPtr,instructionText,'center','center');
+Screen(wPtr, 'Flip');
+KbWait([],3); %Waits for keyboard(any) press
+
+% OPEN
+DrawFormattedText(wPtr,' + ','center','center'); Screen(wPtr, 'Flip'); WaitSecs(1);
+for ai=1:30
+    io64(ioObject,LTP1address,1);  WaitSecs(.05);  io64(ioObject,LTP1address,0); WaitSecs(.95);
+    io64(ioObject,LTP1address,2);  WaitSecs(.05);  io64(ioObject,LTP1address,0); WaitSecs(.95);
+end
+sound(Tone(1,:),echant);
+
+%  ~ SNIP
+

README.md

@@ -1,17 +1,42 @@
----
-dataset_info:
-  features:
-  - name: text
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 16217
-    num_examples: 208
-  download_size: 8833
-  dataset_size: 16217
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: /itet-stor/jlauer/net_scratch/datasets/Cavanagh2018b/train-*
----
+# Cavanagh2018b: EEG Parkinson's Classification Dataset in Resting-State
+The Cavanagh2018b dataset contains resting-state EEG recordings from the same group of people used in the Cavanagh2018a study. Participants included 28 individuals diagnosed with Parkinson's disease and 28 age- and sex-matched control participants. Each subject underwent a 2-minute resting-state EEG recording session with eyes open. These recordings were collected in a seated posture, prior to or following the auditory oddball task, under the same EEG setup (64-channel cap, 500 Hz sampling rate).
+
+This dataset serves as a complementary baseline condition for evaluating spontaneous brain dynamics in Parkinson's disease. Though not central to the novelty task paradigm, resting-state data may be useful for investigations into low-frequency oscillations or non-task-based classification approaches of Parkinson's disease.
+## Paper
+Cavanagh, J. F., Kumar, P., Mueller, A. A., Richardson, S. P., & Mueen, A. (2018). **Diminished EEG habituation to novel events effectively classifies Parkinson's patients**. _Clinical Neurophysiology_, 129(2), 409-418.
+
+DISCLAIMER: We (DISCO) are NOT the owners or creators of this dataset, but we merely uploaded it here, to support our's ("EEG-Bench") and other's work on EEG benchmarking.
+## Dataset Structure
+The `IMPORT_ME_REST.xlsx` file contains some information about the patients with PD (sex, age, medication status at first recording), as well as their matched control patients.
+### Filename Format
+
+```
+[PID]_[SESSION]_PD_REST.mat
+```
+PID is the patient ID (e.g. `813`), while SESSION distinguishes different days of recording (can be `1` or `2` for patients with PD and is always `1` for patients without PD). All patients with PID <= 829 have Parkinson's Disease and all patients with PID >= 890 do NOT have Parkinson's Disease and hence belong to the control group.
+
+### Fields in each File
+A `.mat` file can be read in python as follows:
+```python
+from scipy.io import loadmat
+filename = "813_2_PD_REST.mat"
+mat = loadmat(filename, simplify_cells=True)
+```
+(A field "fieldname" can be read from `mat` as `mat["fieldname"]`.)
+
+Then `mat` contains (among others) the following fields and subfields
+- `EEG`
+    - `data`: EEG data of shape `(#channels, time_len)`. E.g. a recording of 7 minutes with 67 channels and a sampling rate of 500 Hz will have shape `(67, 210000)`.
+    - `event`: Contains a list of dictionaries, each entry (each event) having the following description:
+        - `latency`: The onset of the event, measured as the index in the time-dimension `time_len`. The duration of each event is 1 second. Hence, with a 500 Hz sampling rate, the EEG data `event_data` corresponding to the `i`-th event would be
+            ```
+            start_index = mat["EEG"]["event"][i]["latency"]
+            event_data = numpy.transpose(mat["EEG"]["data"], [1, 2]).reshape([#channels, #trials * trial_len])[:, start_index:start_index+500] # shape (#channels, 500)
+            ```
+        - `type`: The type of event. Can be `"S  1"` / `"S  2"` (patient has eyes open), or `"S  3"` / `"S  4"` (patient has eyes closed).
+    - `chanlocs`: A list of channel descriptors
+    - `nbchan`: Number of channels
+    - `srate`: Sampling Rate (Hz)
+
+## License
+By the original authors of this work, this work has been licensed under the PDDL v1.0 license (see LICENSE.txt).

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