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FeedbackDecode.m
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3943 lines (3605 loc) · 186 KB
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function FeedbackDecode(LabviewIP)
% Starting log file
SS.RootDir = fileparts(mfilename('fullpath'));
SS.LogFID = fopen(fullfile(SS.RootDir,'FeedbackDecode_Log.txt'),'w+');
% Initializing system variables
try
SS = initSystem(LabviewIP,SS);
catch ME
fprintf(SS.LogFID,'message: %s; name: %s; line: %0.0f\r\n',ME.message,ME.stack(1).name,ME.stack(1).line);
fprintf('message: %s; name: %s; line: %0.0f\r\n',ME.message,ME.stack(1).name,ME.stack(1).line);
end
% Initializing timer
t = timer;
t.ExecutionMode = 'fixedRate';
t.Period = SS.BaseLoopTime;
t.TasksToExecute = 1e12;
t.BusyMode = 'drop';
t.TimerFcn = @mainLoop;
t.StopFcn = @closeSystem;
t.UserData = SS;
fprintf('starting timer\n');
start(t);
wait(t);
fprintf('finished timer\n');
% delete(t);
%%%%%%%%%%%%%%%%%%%%%%%%% Timer Callbacks %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function mainLoop(t,~)
try
SS = t.UserData;
SS.MCalcTic = double(xippmex_1_14('time'));
SS = acqEvents(SS);
if SS.Stop
stop(t);
end
SS = acqData(SS);
SS = acqAuxEvents(SS);
SS = acqCont(SS);
SS = acqTraining(SS);
SS = acqBaseline(SS);
SS = runTesting(SS); %SS.XHat
SS = cogLoad(SS);
SS = sendStim(SS);
SS = saveContStim(SS);
SS = saveTask(SS);
SS = savePHand(SS);
SS.MCalcTime = (double(xippmex_1_14('time'))-SS.MCalcTic)/30000; %calculation time within loop
SS.MTotalTime = t.InstantPeriod; %overall loop time
SS.LoopCnt = t.TasksExecuted;
t.UserData = SS;
catch ME
assignin('base','ME',ME)
fprintf(SS.LogFID,'message: %s; name: %s; line: %0.0f\r\n',ME.message,ME.stack(1).name,ME.stack(1).line);
fprintf('message: %s; name: %s; line: %0.0f\r\n',ME.message,ME.stack(1).name,ME.stack(1).line);
t.UserData = SS;
% stop(t);
end
function closeSystem(t,~)
SS = t.UserData;
SS.RecEnd = double(xippmex_1_14('time')); %smw - get end NIP time for Synching purposes
SS = orderfields(SS);
assignin('base','SS',SS)
xippmex_1_14('stim','enable',0); pause(0.1);
try
% xippmex_1_14('trial',SS.XippOpers,'stopped'); pause(0.1);
xippmex_1_14('trial','stopped'); pause(0.1);
catch
disp('xippmex call to stop recording crashed...')
end
xippmex_1_14('close'); clear('xippmex_1_14'); mj_close;
SS.SSFile = fullfile(SS.FullDataFolder,['\SSStruct_',SS.DataFolder,'.mat']);
save(SS.SSFile,'SS');
% fwrite(SS.UDPEvnt,'MatlabReady'); %Tell LV that matlab has shut down
% fwrite(SS.UDPContAux,[zeros(6,1);0;SS.BakeoffDirect],'single')
% fwrite(SS.UDPEvntAux,'Stop:'); %tell MLAux to stop
% fclose(SS.UDPEvnt); fclose(SS.UDPEvntAux); fclose(SS.UDPCont); fclose(SS.UDPContAux); fclose(SS.TaskFID); fclose(SS.ContStimFID); fclose(SS.LogFID);
% delete(SS.UDPEvnt); delete(SS.UDPEvntAux); delete(SS.UDPCont); delete(SS.UDPContAux);
% if isfield(SS,'UDPNIP')
% fclose(SS.UDPNIP);
% delete(SS.UDPNIP);
% end
if SS.ARD1.Ready; SS.ARD1.Ready = ctrlRBArduino; end
if SS.VTStruct.Ready; SS.VTStruct.Obj.close; end
if SS.ARD3.Ready; fclose(SS.ARD3.Obj); delete(SS.ARD3.Obj); end
if isfield(SS,'PHandFID'); fclose(SS.PHandFID); end
if SS.DEKA.Ready; lkmex('stop'); clear lkmex; end
if SS.TASKA.Obj.ready; clear SS.TASKA.Obj; end
% if SS.TASKASensors.Ready; closeTASKASensors_simple(SS.TASKASensors.Obj); delete(SS.TASKASensors.Obj); end % jag 7/26/18
if SS.TASKASensors.Obj.Ready; delete(SS.TASKASensors.Obj); end % ESS 8/18/21
if SS.ConnectECG
if isfield(SS,'shimmerECG')
ecgstop(SS.shimmerECG);
pause(0.01);
ecgdisconnect(SS.shimmerECG);
end
end
if SS.ConnectIMU
if isfield(SS.IMU,'Object')
for i = 1:length(SS.IMU.Object)
imudisconnect(SS.IMU.Object(i));
end
end
end
fclose(SS.DEKAFID);
fclose(SS.TASKAFID);
fclose(SS.CogLoadFID);
fclose(SS.TaskFID);
fclose(SS.ContStimFID);
fclose(SS.LogFID);
fwrite(SS.UDPEvnt,'MatlabReady'); %Tell LV that matlab has shut down
fwrite(SS.UDPContAux,[zeros(6,1);0;SS.BakeoffDirect],'single')
fwrite(SS.UDPEvntAux,'Stop:'); %tell MLAux to stop
fclose(SS.UDPEvnt); fclose(SS.UDPEvntAux); fclose(SS.UDPCont); fclose(SS.UDPContAux);
delete(SS.UDPEvnt); delete(SS.UDPEvntAux); delete(SS.UDPCont); delete(SS.UDPContAux);
if isfield(SS,'UDPNIP')
fclose(SS.UDPNIP);
delete(SS.UDPNIP);
end
% delete(t);
delete(instrfindall);
close all; fclose all;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%% Subfunctions %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Initialization function
function SS = initSystem(LabviewIP,SS)
delete(instrfindall); delete(timerfindall);
xippmex_1_14('close'); clear('xippmex_1_14'); mj_close;
if strcmp(LabviewIP,'127.0.0.1')
SS.LocalIP = '127.0.0.2';
else
% Find local computer name
[~,compstr] = system('powershell get-wmiobject -class win32_computersystem');
SS.LocalComp = cell2mat(regexp(compstr,'Name\s+:\s([^\s]+)','tokens','once'));
% Find ip address of local computer
[~,netstr] = system('powershell ipconfig');
stroffset = regexp(netstr,'Matlab_Network');
SS.LocalIP = regexp(netstr(stroffset:end),'\d+\.\d+\.\d+\.\d+','match'); SS.LocalIP = SS.LocalIP{1};
end
% Saving LabviewIP to structure
SS.LabviewIP = LabviewIP;
% Opening network for UDP communication with LV
warning('off','instrument:fscanf:unsuccessfulRead');
SS.UDPEvnt = udp(SS.LabviewIP,9002,'localhost',SS.LocalIP,'localport',9002); %Sending/receiving string commands
SS.UDPEvnt.InputBufferSize = 65535; SS.UDPEvnt.InputDatagramPacketSize = 13107; SS.UDPEvnt.OutputBufferSize = 65535; SS.UDPEvnt.OutputDatagramPacketSize = 13107;
SS.UDPCont = udp(SS.LabviewIP,9005,'localhost',SS.LocalIP,'localport',9005); %Sending/receiving continuous
SS.UDPCont.InputBufferSize = 65535; SS.UDPCont.InputDatagramPacketSize = 13107; SS.UDPCont.OutputBufferSize = 65535; SS.UDPCont.OutputDatagramPacketSize = 13107;
fopen(SS.UDPEvnt);
fopen(SS.UDPCont);
% Opening network for UDP communication with auxiliary matlab loop
SS.UDPEvntAux = udp('127.0.0.1',9004,'localhost','127.0.0.1','localport',9003); %Sending/receiving string commands to auxiliary matlab loop for delayed processing
SS.UDPEvntAux.InputBufferSize = 65535; SS.UDPEvntAux.InputDatagramPacketSize = 13107; SS.UDPEvntAux.OutputBufferSize = 65535; SS.UDPEvntAux.OutputDatagramPacketSize = 13107;
SS.UDPContAux = udp('127.0.0.1',9007,'localhost','127.0.0.1','localport',9006); %Sending/receiving continuous data for bakeoff tests
SS.UDPContAux.InputBufferSize = 65535; SS.UDPContAux.InputDatagramPacketSize = 13107; SS.UDPContAux.OutputBufferSize = 65535; SS.UDPContAux.OutputDatagramPacketSize = 13107;
fopen(SS.UDPEvntAux);
fopen(SS.UDPContAux);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% COB %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Opening network for UDP with NIP
% SS.UDPNIP = udp('192.168.42.1',5075,'localhost','192.168.42.132','localport',5076);
% SS.UDPNIP.InputBufferSize = 4096; SS.UDPNIP.InputDatagramPacketSize = 1024; SS.UDPNIP.OutputBufferSize = 4096; SS.UDPNIP.OutputDatagramPacketSize = 1024;
% SS.UDPNIP.ByteOrder = 'littleEndian';
% fopen(SS.UDPNIP);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Connecting to NIP
disp('Initializing NIP...')
SS = initNIP(SS);
% Reading initial event values from LV
fwrite(SS.UDPEvnt,'MatlabReady'); %fprintf writes a termination character (fwrite does not)
eval(fscanf(SS.UDPEvnt)); %SS.KalmanElects=[];SS.Lag=0;SS.KernelWidth=0.3;SS.DataFolder='';SS.BadUEAElects=[];SS.SelElect=1;SS.ThreshMode=0;SS.ThreshVal=-150;
% smw to do: list SS.Fields received from labview
% td: Added SS.Prosthesis (i.e. MaleLeft, MaleRight, etc.) from labview. Might want to do something with this variable?
% Initializing file structure variables
if isempty(SS.DataFolder); SS.DataFolder='00000000-000000'; end
SS.DataDir = regexprep(SS.DataDir,'@',':');
SS.BuildDir = regexprep(SS.BuildDir,'@',':');
SS.FullDataFolder = fullfile(SS.DataDir,SS.DataFolder);
if ~isfolder(SS.FullDataFolder)
mkdir(SS.FullDataFolder)
end
% Parsing Map
switch SS.Map
case 'pNeural/pEMG'
SS.MapType.Neural = 'haptix';
SS.MapType.EMG = 'passive';
case 'pNeural/aEMG'
SS.MapType.Neural = 'haptix';
SS.MapType.EMG = 'active';
case 'aNeural/pEMG'
SS.MapType.Neural = 'haptixActive';
SS.MapType.EMG = 'passive';
case 'aNeural/aEMG'
SS.MapType.Neural = 'haptixActive';
SS.MapType.EMG = 'active';
case 'N2021/pEMG'
SS.MapType.Neural = 'haptix2021';
SS.MapType.EMG = 'passive';
otherwise
disp('Incorrect map selected...')
return;
end
if SS.StartMLAux
% system('powershell start-process "D:\RemoteRepo\FeedbackDecodeAux.exe" -verb runAs');
switch SS.NumComp
case '1 Computer'
system(['psexec -i -d -u Administrator -p UUNEL@CNC "',fullfile(SS.BuildDir,'FeedbackDecodeAux.exe"')]);
otherwise
system('psexec -i -d -u Administrator -p UUNEL@CNC D:\RemoteRepo\FeedbackDecodeAux.exe');
end
else
disp('Start auxiliary loop manually');
end
while 1
MLAuxStr = fscanf(SS.UDPEvntAux);
if strcmp(MLAuxStr,'MatlabAuxReady')
break;
else
disp('Waiting for MatlabAux to start...')
end
end
% opening connection with arduino
SS.ARD6.Ready = 0;
SS = connectARD(SS);
pause(2);
% initializing LEAP
SS.LEAP.Ready = 0;
SS = connectLEAP(SS);
% initializing cyberglove
SS.CyberGlove.Ready = 0;
SS.CyberGlove.Kinematics = zeros(4,1);
% Initialize SS.sIMU.Ready
SS.IMU.Ready = 0;
SS.IMU.Calib = 0;
SS.IMU.WaistAngles = [0,0,0];
SS.IMU.ShoulderAngles = [0,0,0];
% Initialize low-cost Nathan Taska wrist TNT 4/7/21
% try
% [SS.LCWrist, SS.LCWrist_LastKin ] = initiateTaskaWrist(); %
% % Commented out by TNT 6/16/22 too slow during startup
% SS.LCWrist_history = zeros(2,20);
% SS.LCWrist_Ready = 1;
% disp("Low-Cost Taska Wrist Connected");
%
% catch
% disp("No Low-Cost Taska Wrist found, IS THE PATH ADDED???");
% SS.LCWrist_Ready = 0;
% end
% Initializing loop timing
SS.BaseLoopTime=0.033; %smw - change to 0.025 at some point?
% SS.BaseLoopTime=0.02; %dk TASKA testing
SS.MCalcTic = double(xippmex_1_14('time'));
SS.MCalcTime = 0;
SS.MTotalTime = 0;
SS.Stop = 0;
SS.LoopCnt = 1; %total loop iterations from start
SS.TrainCnt = 1; %number of loop iterations during training
SS.StartBakeoff = false;
if any(strcmp(SS.CtrlMode,'Velocity'))
SS.BakeoffDirect = 1;
else
SS.BakeoffDirect = 0;
end
SS.PulseStim = 0;
SS.ManualType = 'Max'; %use min/max freq/amp in labview table for stim params
SS.StimWf = zeros(52,1);
% Initializing electrode/chan variables, buffers
SS.Fs = 30000;
SS.FsEMG = 1000;
SS.NumUEAs = 2;
SS.NumNeuralIdxs = 96*SS.NumUEAs;
SS.NumNeuralElects = 100*SS.NumUEAs;
SS.NumNeuralChans = 128*SS.NumUEAs;
SS.NumEMGIdxs = 528; %all possible pair combinations across all 8 leads plus individual
SS.NumDOF = 12; %thumb,index,middle,ring,little,thumbint,indexint,ringint,littleint,wrist,wristyaw,wristroll
SS.ManualDOF = false(SS.NumDOF,1);
SS.DLNeuralMaxMS = ceil(SS.BaseLoopTime*1000*1.1);
SS.DLNeuralMax = SS.DLNeuralMaxMS*(SS.Fs/1000); %max length of data acquired each loop iteration
SS.BadUEAElectsPrev = -1;
SS.BaselineData = zeros(SS.NumNeuralIdxs+SS.NumEMGIdxs,1); %baseline subtraction for features
SS.VRSurrIdxs = zeros(SS.NumNeuralIdxs,SS.NumNeuralIdxs);
% SS.VRSurrIdxsGPU = gpuArray(SS.VRSurrIdxs);% initializing VRsurrIdx
SS.NeuralChanList = mapRippleUEA(1:SS.NumNeuralIdxs,'i2c',SS.MapType.Neural); %list of NIP channels instead of indices to use with mex functions
[~,SS.AvailNeuralIdx] = intersect(SS.NeuralChanList,SS.AvailNeural);
% SS.EMGChanList = (1:32)+256; %NIP channels for EMG
SS.EMGChanList = (1:96)+256; %NIP channels for EMG
[~,SS.AvailEMGIdx] = intersect(SS.EMGChanList,SS.AvailEMG);
% add for VTStim if no neural stim FEs
VTChans = mapRippleUEA(2:7,'e2c',SS.MapType.Neural);
VTChans = reshape(VTChans,1,[]);
if SS.VTStruct.Ready && ~sum(ismember(VTChans, SS.AvailStim))
SS.AvailStim = [VTChans SS.AvailStim];
end
%variables sent to labview (jag 10/4/17)
SS.decodeOutput = zeros(12,1);
% Starting python communication
switch SS.NumComp
case '2 Computers'
python_path = '\\PNIMATLAB\PNIMatlab_R1\decodeenginepython_DO_NOT_DELETE';
try %MP20201223: Compiled MATLAB 2020b was having issues with python.
temppy = py.sys.path;
clear temppy;
catch % set environment if compiled MATLAB isn't there.
pyenv('Version', "\\pnimatlab\Users\Administrator\Anaconda3\envs\decode_env\python.exe");
end
if count(py.sys.path,python_path) == 0
insert(py.sys.path,int32(0),python_path);
end
end
% Set latching filter
SS.LF_C = 1;
SS.LatchingFilter = 0;
SS.CogLoadStimOn = 0;
SS.CogLoadButtonPress = 0;
SS.TargOn = 0;
SS.CogLoadNextStimTS = 0;
rng('shuffle'); % shuffle for CogLoad Secondary Task (MDP 20200125)
% Set Online Adaptation parameters
if isdeployed
temp = csvread('HybridParams.csv');
else
temp = csvread(fullfile(fileparts(mfilename('fullpath')),'dependencies','HybridParams.csv'));
end
numBetas = temp(1);
numTrialsPerBeta = temp(2);
SS.maxBetaIdx = numBetas*numTrialsPerBeta;
tempBetaValues = temp(3:3+numBetas-1);
SS.betaValues = [];
for iBetas = 1:numBetas
SS.betaValues = [SS.betaValues tempBetaValues(iBetas)*ones(1,numTrialsPerBeta)];
end
SS.betaValues = SS.betaValues(randperm(numel(SS.betaValues)));
SS.betaIdx = 1;
disp({'Beta Values:' num2str(SS.betaValues)})
SS.AdaptOnline.ShouldAdapt = 1;
SS.AdaptOnline.AdaptationRate = SS.betaValues(SS.betaIdx);
SS.AdaptOnline.GoalX = [];
rng('shuffle'); % ensure unique seed for randn
disp('Goal Adaptation set')
% Initializing variables
SS = initStim(SS);
SS = initVRE(SS);
SS = initDEKA(SS);
SS = initTASKA(SS); % dk 2018-01-26
SS = initTASKASensors(SS);
SS = initAnalogSensors(SS);
SS = updateIdxs(SS); %creates/updates SS.KalmanChans, SS.SelChan, SS.BadChans, and also parses SS.StimCell
SS = initBuffers(SS);
SS = resetKalman(SS);
SS.GoalNoiseFixed = SS.GoalNoise*randn(1,1) + zeros(size(SS.T(SS.KalmanMvnts)));
SS.GoalNoiseHistory = zeros(12,1);
SS.betaHistory = [];
%%%%%%%%%%%%%%%%%%%%%%%%%%% COB %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% SS = sendDecode2NIP(SS);
% SS.pIdx = 1;
% SS.pBuffSize = 300;
% SS.pBuff = zeros(length(SS.KalmanMvnts),SS.pBuffSize);
% % SS.pBuff = zeros(length(SS.KalmanIdxs),SS.pBuffSize);
% SS.fH = figure('windowstyle','docked');
% SS.aH = axes('parent',SS.fH);
% SS.pH = plot(SS.aH,1:SS.pBuffSize,SS.pBuff');
% hold on
% SS.pH(end+1) = plot(SS.aH,[SS.pIdx,SS.pIdx],[-1000,1000],'r');
% hold off
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Initializing other variables
[SS.bHP,SS.aHP] = butter(4,750/(SS.Fs/2),'high'); %butterworth filter (high-pass 250Hz) to use with FilterM (mex function for filtering)
SS.cHP = zeros(4,SS.NumNeuralIdxs); %initial conditions for filter for all channels
% Sending initial continuous data. This needs to be done so the 1st
% continuous read in LV will be successful, thus starting the synchronized
% passes of continuous data between the two systems.
SS.ContML = [SS.TrainCnt;SS.MCalcTime;SS.MTotalTime;SS.XHat;...
% SS.X;length(SS.NeuralElectRatesMA);SS.NeuralElectRatesMA;SS.EMGPwrMA(1:80);SS.ThreshRMS(SS.SelIdx);...
SS.X;length(SS.NeuralElectRatesMA);SS.NeuralElectRatesMA;SS.EMGPwrMA;SS.ThreshRMS(SS.SelIdx);...
length(SS.SelData);SS.SelData;SS.SelWfs(:)];
% disp(SS.ContML); disp('initsystem')%smw
fwrite(SS.UDPCont,typecast(flipud(single(SS.ContML)),'uint8'));
% Starting task file
SS.TaskFile = fullfile(SS.FullDataFolder,['\TaskData_',SS.DataFolder,'.kdf']);
warning('off','MATLAB:MKDIR:DirectoryExists')
mkdir(fileparts(SS.TaskFile));
SS.TaskFID = fopen(SS.TaskFile,'w+');
fwrite(SS.TaskFID,[1;length(SS.Z);length(SS.X);length(SS.T);length(SS.XHat)],'single'); %writing header (1+(96*NumUEAs+528)+12+12+12)
% Starting physical (3D) hand file
if SS.ARD3.Ready
SS.PHandFile = fullfile(SS.FullDataFolder,['\PHandData_',SS.DataFolder,'.phf']);
mkdir(fileparts(SS.PHandFile));
SS.PHandFID = fopen(SS.PHandFile,'w+');
fwrite(SS.PHandFID,[1;length(SS.PHandContactVals);length(SS.PHandMotorVals)],'single'); %writing header (1+(96*NumUEAs+528)+12+12+12)
end
% Starting continuous stim file
SS.ContStimFile = fullfile(SS.FullDataFolder,['\ContStim_',SS.DataFolder,'.csf']);
SS.ContStimFID = fopen(SS.ContStimFile,'w+');
fwrite(SS.ContStimFID,[1;length(SS.AllContStimAmp);length(SS.AllContStimFreq)],'single'); %saving data to ContStim file (*.csf filespec, see readCSF)
% Start Cognitive Load Params File (MDP 1/21/20)
SS.CogLoadFile = fullfile(SS.FullDataFolder,['\CogLoad_',SS.DataFolder,'.clf']);
mkdir(fileparts(SS.CogLoadFile));
SS.CogLoadFID = fopen(SS.CogLoadFile,'w+');
% Start recording and get time
pause(1)
try
SS.XippTS = double(xippmex_1_14('time'));
SS.RecStart = SS.XippTS; %get time when recording started (skipped if xippmex command fails)
if SS.StartXippRec %only automatically start recording if command sent from LV
% xippmex_1_14('trial',SS.XippOpers,'recording',fullfile(SS.FullDataFolder,[SS.DataFolder,'-']));
% xippmex_1_14('trial','recording',fullfile(SS.FullDataFolder,[SS.DataFolder,'-']));
xippmex_1_14('trial','recording',fullfile(SS.FullDataFolder, SS.DataFolder));
RecStart = SS.RecStart;
save(fullfile(SS.FullDataFolder,['\RecStart_',SS.DataFolder,'.mat']),'RecStart')
end
pause(1); %set to specified file and start recording (remote control must be selected on Trellis)
catch ME
if isempty(ME.stack)
fprintf('message: %s\r\n',ME.message);
else
fprintf('message: %s; name: %s; line: %0.0f\r\n',ME.message,ME.stack(1).name,ME.stack(1).line);
end
end
% Starting VRE
SS = startVRE(SS);
SS = startDEKA(SS);
SS = startTASKA(SS); % dk 2018-01-26
% Start both LV and ML loops at the same time
pause(1)
fwrite(SS.UDPEvnt,'MatlabReady');
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Acquiring data
function SS = acqData(SS)
% Getting neural data from xippmex
SS.DNeural = zeros(SS.NumNeuralIdxs,SS.DLNeuralMax);
if ~isempty(SS.AvailNeural)
[DNeural,DNeuralTS] = xippmex_1_14('cont',SS.AvailNeural,SS.DLNeuralMaxMS,'raw'); SS.CurrTS = double(DNeuralTS); %slow (1.08)
if ~isempty(DNeural)
SS.DNeural(SS.AvailNeuralIdx,:) = DNeural; %slow (0.23)
end
else
SS.CurrTS = double(xippmex_1_14('time'))-SS.DLNeuralMax;
end
SS.ApCell = cell(SS.NumNeuralIdxs,1); SS.ApWfCell = repmat({cell(1,[])},SS.NumNeuralIdxs,1); %slow (0.13)
SS.StCell = cell(SS.NumNeuralIdxs,1); SS.StWfCell = repmat({cell(1,[])},SS.NumNeuralIdxs,1);
% if ~isempty(SS.AvailNeural)
% [~,ApCell,ApWfCell] = xippmex_1_14('spike',SS.AvailNeural,0); %slow (1.78)
% [~,StCell,StWfCell] = xippmex_1_14('spike',SS.AvailNeural,1); %slow (1.57)
% SS.ApCell(SS.AvailNeuralIdx) = ApCell;
% SS.ApWfCell(SS.AvailNeuralIdx) = ApWfCell;
% SS.StCell(SS.AvailNeuralIdx) = StCell;
% SS.StWfCell(SS.AvailNeuralIdx) = StWfCell;
% end
% Getting emg data from xippmex
SS.DEMG = zeros(length(SS.AvailEMG),SS.DLNeuralMaxMS);
if ~isempty(SS.AvailEMG)
DEMG = xippmex_1_14('cont',SS.AvailEMG,SS.DLNeuralMaxMS,'lfp',SS.CurrTS);
if ~isempty(DEMG)
SS.DEMG(SS.AvailEMGIdx,1:size(DEMG,2)) = DEMG;
if strcmp(SS.MapType.EMG,'active')
if length(SS.AvailEMG)<=32
SS.DEMG = SS.DEMG(p2a(1:32),:);
end
end
end
end
% Stopping stim if big red button is pressed
[~,SS.DigIO_TS,SS.DigEvents] = xippmex_1_14('digin');
if ~isempty(SS.DigEvents)
if any([SS.DigEvents.reason]==4) %sma2 -- digital I/O input 2
xippmex_1_14('stim','enable',0);
SS.StimMode = 'Off';
fwrite(SS.UDPEvnt,'StopStim:');
disp('Stopping Stim...')
end
end
% Calculating data length for current iteration
SS.DLNeural = min(floor(abs(SS.CurrTS-SS.XippTS)),SS.DLNeuralMax); SS.DLEMG = floor(SS.DLNeural/30); %samples since last acquisition
SS.XippTS = SS.CurrTS;
if SS.DLNeural
% SS.dNeural = SS.DNeural(:,end-SS.DLNeural+1:end)'; %slow (0.62)
% SS.dEMG = 0.2*SS.DEMG(:,end-SS.DLEMG+1:end)'; % smw- question, what is this 0.2; td: Conversion to uV. For some reason, dNeural doesn't need this (may be fixed in newer xippmex.
SS.dNeural = (SS.DNeural(:,end-SS.DLNeural+1:end)./4)'; %factor 4 needed for xippmex_1_14 (they changed the scaling for some reason)
SS.dEMG = (0.2*SS.DEMG(:,end-SS.DLEMG+1:end)./4)'; %factor 4 needed for xippmex_1_14 (they changed the scaling for some reason)
end
% Filtering and performing CAR
[SS.dNeural,SS.cHP] = FilterX(SS.bHP,SS.aHP,SS.dNeural,SS.cHP);
% [dNeural,cHP] = filter(SS.bHP,SS.aHP,gpuArray(SS.dNeural),SS.cHP);
% SS.dNeural = gather(dNeural);
% SS.cHP = gather(cHP);
if SS.CAR
switch SS.CARType
case {0,'Standard'} %standard
% SS.dNeural = gather((gpuArray(SS.dNeural)*SS.NeuralSurrIdxsGPU))
SS.dNeural = SS.dNeural*SS.NeuralSurrIdxs;
case {1,'NN'} %vr
% SS.dNeural = gather((gpuArray(SS.dNeural)*SS.VRSurrIdxsGPU));
SS.dNeural = SS.dNeural*SS.VRSurrIdxs;
end
end
SS.dNeural(:,SS.BadUEAIdxs) = nan;
% update RMS buffer and calculating spike threshold
SS.NeuralRMSBuff(:,SS.NeuralRMSBuffIdx(1)) = std(SS.dNeural,0,1)'; %slow(0.82) %This method of updating the buffer is faster
SS.NeuralRMSBuffIdx = circshift(SS.NeuralRMSBuffIdx,[0,-1]);
if strcmp(SS.ThreshMode,'RMS Auto')
SS.ThreshRMS(1:SS.NumNeuralIdxs) = mean(SS.NeuralRMSBuff,2)*SS.ThreshVal;
end
% Finding spikes and calculating rates
if SS.NIPSpikes % use NIP spike detection
SS.StTS = unique(cell2mat(SS.StCell'))';
for k=1:SS.NumNeuralIdxs
SS.ApTS = SS.ApCell{k,1};
if ~isempty(SS.ApWfCell{k})
% SS.Wf = SS.ApWfCell{k}{1}(1:48)';
SS.Wf = SS.ApWfCell{k}(1,1:48)';
else
SS.Wf = nan(48,1);
end
if ~isempty(SS.StTS) && ~isempty(SS.ApTS)
SS.NeuralRates(k) = sum(all(abs(bsxfun(@minus,SS.StTS,SS.ApTS))>150,1))/SS.BaseLoopTime; %an attempt to remove stim artifact, ignoring all threshold crossings that occur with in 150 samples of the stim pulse
else
SS.NeuralRates(k) = length(SS.ApTS)/SS.BaseLoopTime;
end
if k==SS.SelIdx
SS.SelWfs = SS.Wf;
end
end
else % use "standard" spike detection
[SS.NeuralRates,SS.WfIdx,SS.NeuralREM,SS.Wf] = findSpikesRealTimeMex(SS.dNeural,SS.ThreshRMS(1:SS.NumNeuralIdxs),SS.NeuralREM,SS.XippTS); %slow (0.34)
if SS.SelIdx>SS.NumNeuralIdxs
SS.SelWfs = nan(48,1);
else
SS.SelWfs = SS.Wf(1:48,SS.SelIdx);
end
SS.NeuralRates = SS.NeuralRates./SS.BaseLoopTime;
end
% Update spike rate buffer
SS.NeuralRatesBuff(:,2:end) = SS.NeuralRatesBuff(:,1:end-1);
SS.NeuralRatesBuff(:,1) = SS.NeuralRates; %current firing rate for all neural indices
SS.NeuralRatesMA = mean(SS.NeuralRatesBuff,2); %moving average firing rate for all neural indices
% Update continuous EMG buffer
% SS.EMGDiffBuff(SS.EMGDiffBuffIdx(1:SS.DLEMG),:) = mtimesx(SS.dEMG,SS.EMGMatrix,'speed');
% SS.EMGDiffBuff(SS.EMGDiffBuffIdx(1:SS.DLEMG),:) = gather(gpuArray(SS.dEMG)*SS.EMGMatrixGPU); %~300x(80+448) (time x all possible diff pairs on a single lead plus all other possible pairs across leads - see genEMGMatrix.m)
if length(SS.AvailEMG)<=32
SS.EMGDiffBuff(SS.EMGDiffBuffIdx(1:SS.DLEMG),:) = SS.dEMG*SS.EMGMatrix; %slow (0.29)
else
SS.EMGDiffBuff(SS.EMGDiffBuffIdx(1:SS.DLEMG),1:length(SS.AvailEMG)) = SS.dEMG;
end
if SS.Log10
SS.EMGPwrMA = mean(10*log10(abs(SS.EMGDiffBuff)+1),1)';
else
SS.EMGPwrMA = mean(abs(SS.EMGDiffBuff),1)'; %slow (0.24) %emg pwr averaged over kernel width
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%
% Subtracting baseline
if SS.ApplyBaseline
SS.NeuralRatesMA = SS.NeuralRatesMA - SS.BaselineData(1:SS.NumNeuralIdxs);
SS.EMGPwrMA = SS.EMGPwrMA - SS.BaselineData((1:SS.NumEMGIdxs)+SS.NumNeuralIdxs);
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Updating kalman
SS.Z = [SS.NeuralRatesMA;SS.EMGPwrMA]; %moving average for spike rate (channel x rate) with emg appended (720 x rate or pwr)
% Updating NN
try
NNdata = circshift(SS.NN.FeatureBuffer,-1,2); %shifts data down (oldest value now at index 1)
if(SS.NN.neuralFeatures) %determine feature size
Features = SS.Z; %all features including neural
else
if(SS.NN.numFeatures == 32)
Features = SS.EMGPwrMA(SE);
else
Features = SS.EMGPwrMA; %first X EMG channels go into buffer for NN
end
end
temp = length(Features);
% discretize?
if(SS.NN.discrete)
steps = SS.NN.steps;
Features(Features <= steps(1)) = 0;
for ii = 1:length(steps)-1
indxs = ( (Features > steps(ii)) & (Features <= steps(ii+1)) );
Features(indxs) = ii;
end
Features(Features > steps(end)) = ii;
end
% scale?
if(SS.NN.scaled)
Features = (Features - SS.NN.minmax(1) ) / SS.NN.minmax(2);
end
% update features
NNdata(1:temp,1) = Features;
% add kinematic features if needed
if(SS.NN.kinematicFeatures)
NNdata(temp+1:end,1) = SS.NN.Prediction;
end
SS.NN.FeatureBuffer = NNdata;
catch
%disp('Failed Updated NN Features');
end
% Updating neural data that are sent continuously to labview
SS.NeuralElectRatesMA(mapRippleUEA(1:SS.NumNeuralIdxs,'i2e',SS.MapType.Neural)) = SS.NeuralRatesMA; %this gets sent back to LV for heatmaps
% Processing data for selected channel
if SS.SelIdx<=SS.NumNeuralIdxs %displaying neural
SS.SelData = [min(SS.dNeural(:,SS.SelIdx));max(SS.dNeural(:,SS.SelIdx))];
elseif SS.SelIdx>SS.NumNeuralIdxs && SS.SelIdx<=(SS.NumNeuralIdxs+SS.NumEMGIdxs) %displaying emg
SS.SelData = [min(SS.EMGDiffBuff(SS.EMGDiffBuffIdx(1:SS.DLEMG),SS.SelIdx-SS.NumNeuralIdxs));max(SS.EMGDiffBuff(SS.EMGDiffBuffIdx(1:SS.DLEMG),SS.SelIdx-SS.NumNeuralIdxs))];
end
% This needs to be shifted after getting selected channel data
SS.EMGDiffBuffIdx = circshift(SS.EMGDiffBuffIdx,[0,-SS.DLEMG]);
% Acquire TASKA Sensor values
%if(SS.TASKASensors.Ready)
% if(SS.TASKASensors.Obj.Ready) %ESS081821
% %prep buffer
% SS.TASKASensors.IRraw = circshift(SS.TASKASensors.IRraw,-1,2);
% SS.TASKASensors.baroraw = circshift(SS.TASKASensors.baroraw,-1,2);
% %read data
% [SS.TASKASensors.IRraw(:,end), SS.TASKASensors.baroraw(:,end)] = readTASKASensors_simple(SS.TASKASensors.Obj,SS.TASKASensors.Count);
% %subtract baseline
% SS.TASKASensors.prevIR = SS.TASKASensors.IR; % TCH 7/7/20
% SS.TASKASensors.IR = median(SS.TASKASensors.IRraw,2) - SS.TASKASensors.BL.IR;% - 0.05*SS.TASKASensors.BL.IR; %subtract baseline and small error window
% SS.TASKASensors.baro = median(SS.TASKASensors.baroraw,2) - SS.TASKASensors.BL.baro;
% SS.TASKASensors.IR(SS.TASKASensors.IR < 0) = 0;
% SS.TASKASensors.baro(SS.TASKASensors.baro < 0) = 0;
% %update thumb pressure to account for drift
% if(SS.TASKASensors.IR(4) <= 40)
% tempP = median(SS.TASKASensors.baroraw,2);
% SS.TASKASensors.BL.baro(4) = tempP(4);
% end
% end
% Acquire TASKA Sensor values
if(SS.TASKASensors.Obj.Ready)
%prep buffer
SS.TASKASensors.IRraw = circshift(SS.TASKASensors.IRraw,-1,2); % this won't really do anything if IRraw is a column vector instead of a buffer
SS.TASKASensors.baroraw = circshift(SS.TASKASensors.baroraw,-1,2);
%read data
SS.TASKASensors.IRraw(:,end) = median(SS.TASKASensors.Obj.Status.IRSmallBuff,2); % median filter of small window of Arduino data
SS.TASKASensors.baroraw(:,end) = median(SS.TASKASensors.Obj.Status.BAROSmallBuff,2);
%subtract baseline from raw IR
SS.TASKASensors.IR = SS.TASKASensors.IRraw(:,end) - SS.TASKASensors.BL.IR;
% if(SS.TASKASensors.ThumbKF.Enabled)
% % subtract baseline for pressure sensors
% SS.TASKASensors.baro = SS.TASKASensors.baroraw(:,end) - SS.TASKASensors.BL.baro;
% % overwrite thumb using KF prediction
% if(SS.TASKASensors.ThumbKF.Init)
% SS.TASKASensors.baro(4) = kalman_test_bias(SS.TASKASensors.baroraw(4),SS.TASKASensors.ThumbKF.TRAIN,[-1,1],1);
% SS.TASKASensors.ThumbKF.Init = 0;
% else
% SS.TASKASensors.baro(4) = kalman_test_bias(SS.TASKASensors.baroraw(4),SS.TASKASensors.ThumbKF.TRAIN,[-1,1],0);
% end
% %scale thumb to 0-10 (otherwise it's 0 to 1)
% SS.TASKASensors.baro(4) = SS.TASKASensors.baro(4)*10;
% % ensure no values below zero
% SS.TASKASensors.IR(SS.TASKASensors.IR < 0) = 0;
% SS.TASKASensors.baro(SS.TASKASensors.baro < 0) = 0;
% else
% set up adaptive baseline for pressure sensor on each digit
if any(SS.TASKASensors.IR > SS.TASKASensors.SharedControl.IRMin) % check to see if any digits have high IR
update_idx = SS.TASKASensors.IR <= SS.TASKASensors.SharedControl.IRMin; % only update those with low IR
else
% if none have high IR, just update pressure baseline to be previous baro value
update_idx = true(4,1);
end
SS.TASKASensors.BL.baro(update_idx) = SS.TASKASensors.baroraw(update_idx,end-1);
% disp(SS.TASKASensors.BL.baro(4));
SS.TASKASensors.baro = SS.TASKASensors.baroraw(:,end) - SS.TASKASensors.BL.baro;
% ensure no values below zero
SS.TASKASensors.IR(SS.TASKASensors.IR < 0) = 0;
% SS.TASKASensors.baro(SS.TASKASensors.baro < 0) = 0;
% run high-pass filter for pressure data
% [SS.TASKASensors.press_filt, SS.TASKASensors.diff_baro] = mtHPFilt(SS.TASKASensors);
% SS.TASKASensors.prevbaro = SS.TASKASensors.baro; % save current baro for next loop
SS.TASKASensors.press_filt = SS.TASKASensors.baro;
SS.TASKASensors.baro = max(SS.TASKASensors.press_filt, 0);
end
% IMU Stuff
% pull live IMU data, compute kinematics, send to decode, etc
try
if SS.IMU.Ready
newIMUdata = [];
for index = 1:length(SS.IMU.Object)
newstuff = SS.IMU.Object(index).getdata('c');
if ~isempty(newstuff)
newIMUdata = [newIMUdata newstuff(end,:)];
else
newIMUdata = [newIMUdata SS.IMU.Data(1+14*(index-1):14+14*(index-1))];
end
end
% perform joint angle calculations
R1 = quat2rnew(newIMUdata(end,11:14));
R2 = quat2rnew(newIMUdata(end,25:28));
R3 = quat2rnew(newIMUdata(end,39:42));
if ~SS.IMU.Calib % check if calibration frame has been set
R12 = R1'*R2;
R23 = R2'*R3;
else
R1fix=R1'*SS.IMU.CalibrMat(:,:,1);
R2fix=R2'*SS.IMU.CalibrMat(:,:,2);
R3fix=R3'*SS.IMU.CalibrMat(:,:,3);
R12=R2fix'*R1fix;
R23=R3fix'*R2fix;
end
[a12,b12,g12] = R2abgtests(R12, 1);
[a23,b23,g23] = R2abgtests(R23, 1);
SS.IMU.WaistAngles = [a12,b12,g12]*180/pi;
SS.IMU.ShoulderAngles = [a23,b23,g23]*180/pi;
SS.IMU.Data = [newIMUdata, SS.IMU.WaistAngles, SS.IMU.ShoulderAngles];
end
catch
disp('IMU Failure')
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Read parameters from Labview for Kalman control
function SS = acqEvents(SS)
% Waiting for UDP packet from LV
if SS.UDPEvnt.BytesAvailable
LVStr = fscanf(SS.UDPEvnt); % SS.Elect=17; or AbortStim: or SendStim:SS.Elect=[35,36,];...
disp(LVStr);
try
% Parsing LVStr
LVCell = regexp(LVStr,':','split','once');
eval(LVCell{length(LVCell)}) %saving variables directly to SS structure
SS = updateIdxs(SS); %any time an electrode number is transmitted, it will be immediately converted to an index into the data matrices
% Execute cases that match term preceding ":" operator
switch LVCell{1}
case 'AlignData'
fwrite(SS.UDPEvntAux,sprintf('AlignData:SS.KDFTrainFile=''%s'';SS.AlignType=''%s'';SS.AutoThresh=%1.1f;SS.BadKalmanIdxs=[%s];',...
regexprep(SS.KDFTrainFile,':','@'), SS.AlignType,SS.AutoThresh,...
regexprep(num2str(SS.BadKalmanIdxs(:)'),'\s+',',')));
disp('Calling AlignData case in AUX Loop')
case 'ApplyTraining'
SS = resetKalman(SS);
if isfield(SS,'KDFTrainFile')
SS.TrainParamsFile = [SS.KDFTrainFile(1:end-4),'_',datestr(clock,'HHMMSS'),'.mat'];
save(SS.TrainParamsFile,'-struct','SS','Lag','KernelWidth','TargRad','ApplyBaseline','BaselineData','KalmanElects','KalmanEMG','KalmanIdxs','BadUEAElects','KEMGExtra','BadEMGChans','KalmanMvnts','KalmanGain','KalmanThresh','KalmanType','ReTrain','TRAIN','KDFTrainFile','KEFTrainFile','BaseLoopTime','NumDOF','NumNeuralIdxs','NumEMGIdxs','FreeParam')
switch SS.KalmanType
case {0,'Standard'} %standard
fwrite(SS.UDPEvntAux,sprintf('KalmanTrainStandard:SS.TrainParamsFile=''%s'';',regexprep(SS.TrainParamsFile,':','@')));
disp('Calling KalmanTrainStandard case in AUX Loop')
case {1,'Mean'} %mean
fwrite(SS.UDPEvntAux,sprintf('KalmanTrainMean:SS.TrainParamsFile=''%s'';',regexprep(SS.TrainParamsFile,':','@')));
disp('Calling KalmanTrainMean case in AUX Loop')
case {2,'Refit'} %refit
fwrite(SS.UDPEvntAux,sprintf('KalmanTrainRefit:SS.TrainParamsFile=''%s'';',regexprep(SS.TrainParamsFile,':','@')));
disp('Calling KalmanTrainReFit case in AUX Loop')
case {3,'LinSVReg'} %LinSVReg ,to do: type 3 on LV, receive aux event
fwrite(SS.UDPEvntAux,sprintf('LinSVReg_train:SS.TrainParamsFile=''%s''; ', regexprep(SS.TrainParamsFile,':','@')));
disp('Calling LinSVReg_train function from AUX Loop')
case {4,'NonLinSVReg'} %NonLinSvm
fwrite(SS.UDPEvntAux,sprintf('NonLinSVReg_train:SS.TrainParamsFile=''%s''; ', regexprep(SS.TrainParamsFile,':','@')));
disp('Calling NonLinSVReg_train function from AUX loop')
case {6,'DWPRR'} % smw 1/11/17
fwrite(SS.UDPEvntAux,sprintf('KalmanTrain_DWPRR:SS.TrainParamsFile=''%s'';',regexprep(SS.TrainParamsFile,':','@')));
disp('Calling KalmanTrain_DWPRR case in AUX Loop')
case {7,'AdaptKF'}
fwrite(SS.UDPEvntAux,sprintf('KalmanTrainStandard:SS.TrainParamsFile=''%s'';',regexprep(SS.TrainParamsFile,':','@')));
disp('Calling KalmanTrainStandard case in AUX Loop')
temp = csvread('HybridParams.csv');
numBetas = temp(1);
numTrialsPerBeta = temp(2);
SS.maxBetaIdx = numBetas*numTrialsPerBeta;
tempBetaValues = temp(3:3+numBetas-1);
SS.betaValues = [];
for iBetas = 1:numBetas
SS.betaValues = [SS.betaValues...
tempBetaValues(iBetas)*ones(1,numTrialsPerBeta)];
end
SS.betaValues = SS.betaValues(randperm(numel(SS.betaValues)));
SS.betaIdx = 1;
disp({'Beta Values:' num2str(SS.betaValues)})
case {9,'NN_python'}
fwrite(SS.UDPEvntAux,sprintf('NN_pythonTrain:SS.TrainParamsFile=''%s'';',regexprep(SS.TrainParamsFile,':','@')));
disp('Calling NN_pythonTrain case in AUX Loop')
temp = csvread('HybridParams.csv');
numBetas = temp(1);
numTrialsPerBeta = temp(2);
SS.maxBetaIdx = numBetas*numTrialsPerBeta;
tempBetaValues = temp(3:3+numBetas-1);
SS.betaValues = [];
for iBetas = 1:numBetas
SS.betaValues = [SS.betaValues...
tempBetaValues(iBetas)*ones(1,numTrialsPerBeta)];
end
SS.betaValues = SS.betaValues(randperm(numel(SS.betaValues)));
SS.betaIdx = 1;
disp({'Beta Values:' num2str(SS.betaValues)})
case {10,'AdaptKF2'}
fwrite(SS.UDPEvntAux,sprintf('KalmanTrainStandard:SS.TrainParamsFile=''%s'';',regexprep(SS.TrainParamsFile,':','@')));
disp('Calling KalmanTrainStandard case in AUX Loop')
temp = csvread('HybridParams.csv');
numBetas = temp(1);
numTrialsPerBeta = temp(2);
SS.maxBetaIdx = numBetas*numTrialsPerBeta;
tempBetaValues = temp(3:3+numBetas-1);
SS.betaValues = [];
for iBetas = 1:numBetas
SS.betaValues = [SS.betaValues...
tempBetaValues(iBetas)*ones(1,numTrialsPerBeta)];
end
SS.betaValues = SS.betaValues(randperm(numel(SS.betaValues)));
SS.betaIdx = 1;
disp({'Beta Values:' num2str(SS.betaValues)})
case {11,'KF_Short_Goal'}
fwrite(SS.UDPEvntAux,sprintf('NN_python_classifier_Train:SS.TrainParamsFile=''%s'';',regexprep(SS.TrainParamsFile,':','@')));
disp('Calling NN_pythonTrain MLP classifier case in AUX Loop')
temp = csvread('HybridParams.csv');
numBetas = temp(1);
numTrialsPerBeta = temp(2);
SS.maxBetaIdx = numBetas*numTrialsPerBeta;
tempBetaValues = temp(3:3+numBetas-1);
SS.betaValues = [];
for iBetas = 1:numBetas
SS.betaValues = [SS.betaValues...
tempBetaValues(iBetas)*ones(1,numTrialsPerBeta)];
end
SS.betaValues = SS.betaValues(randperm(numel(SS.betaValues)));
SS.betaIdx = 1;
disp({'Beta Values:' num2str(SS.betaValues)})
end
else
fwrite(SS.UDPEvnt,'TrainingFinished:'); %nothing happens if apply training is pressed and no training file exists
end
disp('Training Applied')
case 'AutoPop'
switch SS.AutoPopType
case {0,'Standard'} % 'Standard'
disp('Calling Standard AutoPop function')
fwrite(SS.UDPEvntAux,sprintf('AutoPopStandard:SS.KDFTrainFile=''%s'';SS.MapType.Neural=''%s'';SS.AutoThresh=%0.2f;SS.BadKalmanIdxs=[%s];',regexprep(SS.KDFTrainFile,':','@'),SS.MapType.Neural,SS.AutoThresh,regexprep(num2str(SS.BadKalmanIdxs(:)'),'\s+',',')))
case {1,'Stepwise'} % 'Stepwise'
disp('Calling Stepwise AutoPop function')
fwrite(SS.UDPEvntAux,sprintf('AutoPopStepwise:SS.KDFTrainFile=''%s'';SS.MapType.Neural=''%s'';SS.AutoThresh=%0.2f;SS.BadKalmanIdxs=[%s];',regexprep(SS.KDFTrainFile,':','@'),SS.MapType.Neural,SS.AutoThresh,regexprep(num2str(SS.BadKalmanIdxs(:)'),'\s+',',')))
case {2,'Gram'} % smw 1/10/17
disp('Calling Gram-SchmidtDarpa AutoPop function')
try
if(SS.ExcludeSE)
temp = unique([SS.BadKalmanIdxs; 192+SE']);
else
temp = SS.BadKalmanIdxs;
end
catch
temp = SS.BadKalmanIdxs;
end
fwrite(SS.UDPEvntAux,sprintf('AutoPopGram:SS.KDFTrainFile=''%s'';SS.MapType.Neural=''%s'';SS.AutoThresh=%0.2f;SS.BadKalmanIdxs=[%s];',regexprep(SS.KDFTrainFile,':','@'),SS.MapType.Neural,SS.AutoThresh,regexprep(num2str(temp(:)'),'\s+',','))); % note SS.AutoThresh not necessary here
end
case 'ChangeKalmanType' %since kalman testing is always running, if kalman type is changed, the variables must be initialized to match the specific algorithm
SS = resetKalman(SS);
disp('Changing Kalman Type')
case 'ChangeLag'
disp('Changing lag')
case 'ChangeTargRad'
if SS.VRETargetsEnabled
if SS.TargRad <= 0.15
SS.TargSize = 'S';
SS.CurVRETargetIdx = SS.VRETargetIdx(1:6);
SS.HiddenVRETargetIdx = SS.VRETargetIdx(7:18);
elseif SS.TargRad > 0.15 && SS.TargRad <= 0.18
SS.TargSize = 'M';
SS.CurVRETargetIdx = SS.VRETargetIdx(7:12);
SS.HiddenVRETargetIdx = SS.VRETargetIdx(1:6,13:18);
else
SS.TargSize = 'L';
SS.CurVRETargetIdx = SS.VRETargetIdx(13:18);
SS.HiddenVRETargetIdx = SS.VRETargetIdx(1:12);
end
for k = 1:length(SS.HiddenVRETargetIdx) % turn off hidden, other size targets
mj_set_rgba('geom',SS.HiddenVRETargetIdx(k),[0 0 0 0]);
end
for k = 1:length(SS.CurVRETargetIdx) % turn on cur targets
mj_set_rgba('geom',SS.CurVRETargetIdx(k),[0 1 0 0.5]);
end
end
disp('Changing TargRad')
case 'ChangeKernel'
SS.NeuralRatesBuff = zeros(SS.NumNeuralIdxs,round(SS.KernelWidth/SS.BaseLoopTime));
SS.EMGDiffBuff = zeros(floor(SS.KernelWidth*SS.FsEMG),size(SS.EMGMatrix,2)); SS.EMGDiffBuffIdx = 1:size(SS.EMGDiffBuff,1);
disp('Changing kernel width')
case 'EnableCAR'
disp('Enabling CAR')
case {'EnableLog10','DisableLog10'}
if SS.Log10
disp('Enabling Log10')
else
disp('Disabling Log10')
end
case 'EEGTrigger'
if SS.EEGTrigger
disp('EEG Trigger on')
else
disp('EEG Trigger off')
end
case 'EndTrial' %acquiring endtrial event
GNF = zeros(12,1); GNF(SS.KalmanMvnts) = SS.GoalNoiseFixed;
SS.GoalNoiseHistory = [SS.GoalNoiseHistory,GNF];
SS.GoalNoiseFixed = SS.GoalNoise*randn(1,1) + zeros(size(SS.T(SS.KalmanMvnts)));
SS.betaHistory = [SS.betaHistory SS.betaValues(SS.betaIdx)];
SS.betaIdx = SS.betaIdx + 1;
if SS.betaIdx>SS.maxBetaIdx
SS.betaValues = SS.betaValues(randperm(numel(SS.betaValues)));
SS.betaIdx = 1;
disp({'Beta Values:' num2str(SS.betaValues)})
end
SS.AdaptOnline.AdaptationRate = SS.betaValues(SS.betaIdx);
if SS.AcqTraining && strcmp(SS.KinSrc,'Training')
if exist(SS.KEFTrainFile,'file')
fprintf(SS.KEFTrainFID,'SS.TrialTS=%0.0f;%s\r\n',SS.XippTS-SS.RecStart,LVCell{length(LVCell)});
end
end
if SS.EEGTrigger && (strcmp(SS.KinSrc, 'Decode'))
% send stop trigger
disp('Stop EEG Trigger')
xippmex_1_14('digout',5,targ2EEGEvent(SS.TargRad, SS.T, 'TargOff'))
end